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Ex Vivo Biosignatures

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Health and Wellness Measurement Approaches for Mobile Healthcare

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

Abstract

In this chapter, mhealth solutions for remote monitoring of ex vivo biosignatures is initially discussed in lieu of instrumentation. A summary of versatile sensor technologies that incorporate numerous features and the recommended methods for the critical steps of data acquisition in real-world settings is presented. The technology advancement herein refers to the design enhancement of sensors per se as well as improving the software from different aspects including noise filtering, feature extraction and classification of ex vivo biosignatures. It has been noted that the broad sphere of sensor technologies empowers the technology developers with various modes of data acquisition using sensors, for example, embedded in smartphones or clothing. The majority of studies were carried out in laboratory settings; however, there are recommendations for the development of robust sensor platforms that can integrate into long-term real-world applications. Finally, the growing body of research on the usability and clinical efficacy of mhealth interventions for a wide range of health disorders are reviewed. This affirms the potential of mhealth in enhancing the value proposition of healthcare through a range of remote healthcare options from people seeking health information to assisting with diagnosis, managing health risks and treatment.

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References

  1. Kaniusas E (2012) Biomedical signals and sensors I: linking physiological phenomena and biosignals. Springer, Heidelberg

    Book  Google Scholar 

  2. Huang M-C, Liu JJ, Xu W, Gu C, Li C, Sarrafzadeh M (2016) A self-calibrating radar sensor system for measuring vital signs. IEEE Trans Biomed Circuits Syst 10(2):352–363

    Article  Google Scholar 

  3. Kranjec J, Beguš S, Geršak G, Drnovšek J (2014) Non-contact heart rate and heart rate variability measurements: a review. Biomed Signal Process Control 13:102–112

    Article  Google Scholar 

  4. Dosinas A, Vaitkūnas M, Daunoras J (2006) Measurement of human physiological parameters in the systems of active clothing and wearable technologies. Elektron Elektrotech 71(7):77–82

    Google Scholar 

  5. Han T, Xiao X, Shi L, Canny J, Wang J (2015) Balancing accuracy and fun: designing camera based mobile games for implicit heart rate monitoring. In: 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, South Korea, 18–23 April 2015

    Google Scholar 

  6. Abe E, Chigira H, Fujiwarai K, Yamakawa T, Kano M (2015) Heart rate monitoring by a pulse sensor embedded game controller. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Hong Kong, China, 16–19 December 2015

    Google Scholar 

  7. Soares RT, Siqueira ES, Miura MA, e Silva TP, Castanho CD (2016) Biofeedback sensors in game telemetry research. http://www.sbgames.org/sbgames2016/downloads/anais/157482.pdf. Accessed Mar 2018

  8. Sun Y, Thakor N (2016) Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans Biomed Eng 63(3):463–477

    Article  Google Scholar 

  9. Katona J, Farkas I, Ujbanyi T, Dukan P, Kovari A (2014) Evaluation of the NeuroSky MindFlex EEG headset brain waves data. In: IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Herl’any, Slovakia, 23–25 January 2014

    Google Scholar 

  10. Siswoyo A, Arief Z, Sulistijono IA (2017) Application of artificial neural networks in modeling direction wheelchairs using neurosky mindset mobile (EEG) device. EMITTER 5(1):170–191

    Article  Google Scholar 

  11. Tiwari K, Saini S (2015) Brain controlled robot using neurosky mindwave. JTASR 1(4):328–331

    Google Scholar 

  12. NeuroSky (2018) Brainwaves; not thoughts. http://neurosky.com/biosensors/eeg-sensor/. Accessed Mar 2018

  13. Dave P (2015) Augmented reality start-up Daqri acquires smart headband maker Melon. Los Angeles Times. http://www.latimes.com/business/technology/la-fi-tn-daqri-melon-20150219-story.html. Accessed Mar 2018

  14. MUSE (2018) Muse: the brain sensing headband. http://www.choosemuse.com/. Accessed Mar 2018

  15. Emotive (2018) Emotive BrainWear. https://www.emotiv.com/. Accessed Mar 2018

  16. Byrom B, Mc Carthy M, Schuleler P, Muehlhausen W (2018) Brain monitoring devices in neuroscience clinical research: the potential of remote monitoring using sensors, wearables and mobile devices. Clin Pharmacol Ther 104(1):59–71

    Article  Google Scholar 

  17. Breuer T, Bruells CS, Rossaint R, Steffen H, Disselhorst-Klug C, Czaplik M, Zoremba N (2017) Acceleration sensors in abdominal wall position as a non-invasive approach to detect early breathing alterations induced by intolerance of increased airway resistance. J Cardiothorac Surg 12(1):96. https://doi.org/10.1186/s13019-017-0658-5

    Article  Google Scholar 

  18. Aly H, Youssef M (2016) Zephyr: ubiquitous accurate multi-sensor fusion-based respiratory rate estimation using smartphones. In: The 35th Annual IEEE International Conference on Computer Communications, San Francisco, USA, 10–14 April 2016

    Google Scholar 

  19. Spiro (2018) Make your clothes smart. https://spire.io/. Accessed Mar 2018

  20. ScienceDaily (2015) Wearable sensor clears path to long-term EKG, EMG monitoring. http://www.sciencedaily.com/releases/2015/01/150120102500.htm. Accessed Dec 2015

  21. Gupta SKS, Mukherjee T, Venkatasubramanian KK (2013) Body area networks: safety, security, and sustainability. Cambridge University Press, UK

    Book  Google Scholar 

  22. Vital Jacket (2015) Biodevices vital jacket—the future of heart monitoring. http://www.vitaljacket.com/?page_id=156. Accessed Dec 2015

  23. Hornyak T (2013) Undershirt monitors heart rate with wearable electrodes. http://www.cnet.com/news/undershirt-monitors-heart-rate-with-wearable-electrodes/. Accessed Dec 2015

  24. Paradiso R, Loriga G, Taccini N, Pacelli M, Orselli R (2004) Wearable system for vital signs monitoring. Stud Health Technol Inform 108:253–259

    Google Scholar 

  25. Pandian P, Mohanavelu K, Safeer K, Kotresh T, Shakunthala D, Gopal P, Padaki V (2008) Smart Vest: wearable multi-parameter remote physiological monitoring system. Med Eng Phys 30(4):466–477

    Article  Google Scholar 

  26. Di Rienzo M, Racca V, Rizzo F, Bordoni B, Parati G, Castiglioni P, Meriggi P, Ferratini M (2013) Evaluation of a textile-based wearable system for the electrocardiogram monitoring in cardiac patients. Europace 15(4):607–612

    Article  Google Scholar 

  27. Morrison T, Silver J, Otis B (2014) A single-chip encrypted wireless 12-lead ECG smart shirt for continuous health monitoring. In: Symposium on VLSI Circuits Digest of Technical Papers, Honolulu, USA, 10–13 June 2014

    Google Scholar 

  28. HealthWatch (2014) hWear Digital Garments. http://www.personal-healthwatch.com/hwear-health-sensing-garments.aspx. Accessed Dec 2015

  29. Jones, MT, Martin, TL (2009) Hardware and Software Architectures for Electronic Textiles. In: Cho G (ed) Smart clothing: technology and applications. CRC Press, USA

    Book  Google Scholar 

  30. Redmond S, Ee Y, Basilakis J, Celler B, Lovell N (2009) ECG recording and rhythm analysis for distributed health care environments. In: Acharya UR, Tamura T, Ng EYK, Min LC, Sure JS (eds) Distributed diagnosis and home healthcare. American Scientific, USA

    Google Scholar 

  31. Srikureja W, Darbar D, Reeder GS (2000) Tremor-induced ECG artifact mimicking ventricular tachycardia. Circulation 102(11):1337–1338

    Article  Google Scholar 

  32. Edelberg R (1973) Local electrical response of the skin to deformation. J Appl Physiol 34(3):334–340

    Article  Google Scholar 

  33. Finni T, Hu M, Kettunen P, Vilavuo T, Cheng S (2007) Measurement of EMG activity with textile electrodes embedded into clothing. Physiol Meas 28(11):1405

    Article  Google Scholar 

  34. Myontec (2018) MBODY AllSport 6 Channel. https://www.myontec.com/products/mbody/. Accessed Mar 2018

  35. Düking P, Hotho A, Holmberg H-C, Fuss FK, Sperlich B (2016) Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Front Physiol 7:71. https://doi.org/10.3389/fphys.2016.00071

    Article  Google Scholar 

  36. De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13(2):135–163

    Article  Google Scholar 

  37. Belbasis A, Fuss FK (2015) Development of next-generation compression apparel. Proc Tech 20:85–90

    Article  Google Scholar 

  38. Colyer SL, McGuigan PM (2018) Textile electrodes embedded in clothing: a practical alternative to traditional surface electromyography when assessing muscle excitation during functional movements. J Sports Sci Med 17(1):101–109

    Google Scholar 

  39. Hakonen M, Piitulainen H, Visala A (2015) Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control 18:334–359

    Article  Google Scholar 

  40. Burgess SW, Wilson SS, Cooper DM, Sly PD, Devadason SG (2006) In vitro evaluation of an asthma dosing device: the smart-inhaler. Resp Med 100(5):841–845

    Article  Google Scholar 

  41. Perez C (2015) Smart inhalers and the future of respiratory health management: smart inhalers are part of a new wave of digital technology designed to improve the management of lung diseases. In: RT for Decision Makers in Respiratory Care 28(10):10–14. http://www.rtmagazine.com/2015/10/smart-inhalers-future-respiratory-health-management/. Accessed Mar 2018

  42. Chen C-C, Liu Y-J, Sung G-N, Yang C-C, Wu C-M, Huang C-M (2015) Smart electronic dose counter for pressurized metered dose inhaler. In: IEEE Biomedical Circuits and Systems Conference, Atlanta, USA, 22–24 October 2015

    Google Scholar 

  43. Furst SJ, Seelecke S (2014) Fabrication and characterization of a dual-joint smart inhaler nozzle actuated by embedded SMA wires. Smart Mater Struct 23(3):035008

    Article  Google Scholar 

  44. Howard S, Lang A, Sharples S, Shaw D (2017) See I told you I was taking it!—attitudes of adolescents with asthma towards a device monitoring their inhaler use: implications for future design. Appl Ergon 58:224–237

    Article  Google Scholar 

  45. Kikidis D, Konstantinos V, Tzovaras D, Usmani OS (2016) The digital asthma patient: the history and future of inhaler based health monitoring devices. J Aerosol Med Pulm Drug Deliv 29(3):219–232

    Article  Google Scholar 

  46. Asthma UK (2017) Smart asthma: real-world implementation of connected devices in the UK to reduce asthma attacks. https://www.asthma.org.uk/globalassets/get-involved/external-affairs-campaigns/publications/smart-asthma/auk_smartasthma_feb2017.pdf. Accessed Mar 2018

  47. Chamberlain D, Jimenez-Galindo A, Fletcher RR, Kodgule R (2016) Applying augmented reality to enable automated and low-cost data capture from medical devices. In: Proceedings of the 8th International Conference on Information and Communication Technologies and Development, Ann Arbor, USA, 3–6 June 2016

    Google Scholar 

  48. Kassem A, Hamad M, El-Moucary C, Neghawi E, Jaoude GB, Merhej C (2013) Asthma care apps. In: 2nd International Conference on Advances in Biomedical Engineering, Tripoli, Lebanon, 11–13 September 2013

    Google Scholar 

  49. Kassem A, Hamad M, El Moucary C (2015) A smart spirometry device for asthma diagnosis. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 25–29 August 2015

    Google Scholar 

  50. Carspecken CW, Arteta C, Clifford GD (2013) TeleSpiro: a low-cost mobile spirometer for resource-limited settings. In: IEEE Point-of-Care Healthcare Technologies, Bangalore, India, 16–18 January 2013

    Google Scholar 

  51. Gupta S, Chang P, Anyigbo N, Sabharwal A (2011) mobileSpiro: accurate mobile spirometry for self-management of asthma. In: Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, Seattle, USA, 1 November 2011

    Google Scholar 

  52. Bumatay A, Chan R, Lauher K, Kwan AM, Stoltz T, Delplanque J-P, Kenyon NJ, Davis CE (2012) Coupled mobile phone platform with peak flow meter enables real-time lung function assessment. IEEE Sens J 12(3):685–691

    Article  Google Scholar 

  53. Natarajan S, Castner J, Titus A (2016) Smart phone-based peak expiratory flow meter. Electron Lett 52(11):904–905

    Article  Google Scholar 

  54. HealthUp (2017) MySpiroo. http://www.myspiroo.com/#About. Accessed Mar 2018

  55. Smart Peak Flow™ (2017) Asthma control in your pocket. http://www.smartpeakflow.com/. Accessed Mar 2018

  56. MIR Medical International Research (2017) SMARTONE®. https://www.spirometry.com/eng/products/smartone.asp. Accessed Mar 2018

  57. Wing (2017) Don’t let COPD land you in the hospital. https://mywing.io/. Accessed Mar 2018

  58. Schneider AM (2015) Personalized asthma medication. Dissertation, Umeå University

    Google Scholar 

  59. Blake WK (1986) Mechanics of flow-induced sound and vibration: complex flow-structure interactions. Academic Press, USA

    MATH  Google Scholar 

  60. Hardin J, Patterson J (1979) Monitoring the state of the human airways by analysis of respiratory sound. Acta Astronaut 6(9):1137–1151

    Article  Google Scholar 

  61. Luisada A (1964) The areas of auscultation and the two main heart sounds. Med Times 92:8–11

    Google Scholar 

  62. Sprague HB, Ongley PA (1954) The clinical value of phonocardiography. Circulation 9(1):127–134

    Article  Google Scholar 

  63. Littmann® M Electronic Stethoscopes (2015) 3M™ Littmann®. https://www.littmann.com/3M/en_US/littmann-stethoscopes/products/~/3M-Littmann-Stethoscopes/Electronic-Stethoscopes/?N=5142935+8711017+8727094+3294857497&rt=r3. Accessed Dec 2016

  64. Jatupaiboon N, Pan-Ngum S, Israsena P (2010) Electronic stethoscope prototype with adaptive noise cancellation. In: 8th International Conference on ICT and Knowledge Engineering, Bangkok, Thailand, 24–25 November 2010

    Google Scholar 

  65. Ghavami M (1998) Adaptive line enhancement using a parallel iir filter with a step-by-step algorithm. Int J Eng 11(2):73

    Google Scholar 

  66. Lakhe A, Sodhi I, Warrier J, Sinha V (2016) Development of digital stethoscope for telemedicine. J Med Eng Technol 40(1):20–24

    Article  Google Scholar 

  67. Gnitecki J, Moussavi ZM (2007) Separating heart sounds from lung sounds. IEEE Eng Med Biol Mag 26(1):20–29

    Article  Google Scholar 

  68. Gupta S, Pandey S, Jiavana FK (2016) Low noise electronic stethoscope. Adv Nat Appl Sci 10(14):52–58

    Google Scholar 

  69. Das D, Banerjee R, Choudhury AD, Deshpande P, Shah N, Date V, Pal A, Mandana KM (2017) Noise detection in smartphone phonocardiogram. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 5–9 March 2017

    Google Scholar 

  70. Das D, Banerjee R, Choudhury AD, Bhattacharya S, Deshpande P, Pal A, Mandana KM (2017) Novel features from autocorrelation and spectrum to classify Phonocardiogram quality. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, 11–15 July 2017

    Google Scholar 

  71. Springer DB, Brennan T, Zuhlke LJ, Abdelrahman HY, Ntusi N, Clifford GD, Mayosi BM, Tarassenko L (2014) Signal quality classification of mobile phone-recorded phonocardiogram signals. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 4–9 May 2014

    Google Scholar 

  72. Springer DB, Brennan T, Ntusi N, Abdelrahman HY, Zühlke LJ, Mayosi BM, Tarassenko L, Clifford GD (2016) Automated signal quality assessment of mobile phone-recorded heart sound signals. J Med Eng Technol 40(7–8):342–355

    Article  Google Scholar 

  73. Kumar D, Carvalho P, Antunes M, Paiva R, Henriques J (2011) Noise detection during heart sound recording using periodicity signatures. Physiol Meas 32(5):599–618

    Article  Google Scholar 

  74. Thinklabs (2017) Thinklabs One Digital Stethoscope. http://www.thinklabs.com/one-digital-stethoscope. Accessed Aug 2017

  75. HD Medical (2017) ViScope MD. http://hdmedicalgroup.com/our-products/viscope-md/. Accessed Aug 2017

  76. Sinharay A, Ghosh D, Deshpande P, Alam S, Banerjee R, Pal A (2016) Smartphone based digital stethoscope for connected health—a direct acoustic coupling technique. In: IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, Washington, USA, 27–29 June 2016

    Google Scholar 

  77. Aguilera-Astudillo C, Chavez-Campos M, Gonzalez-Suarez A, Garcia-Cordero JL (2016) A low-cost 3-D printed stethoscope connected to a smartphone. In: 38th Annual International Conference of Engineering in Medicine and Biology Society, Orlando, USA, 16–20 August 2016

    Google Scholar 

  78. Gauthier D, Akay YM, Paden RG, Pavlicek W, Fortuin FD, Sweeney JK, Lee RW, Akay M (2007) Spectral analysis of heart sounds associated with coronary occlusions. In: 6th International Special Topic Conference on Information Technology Applications in Biomedicine, Tokyo, Japan, 8–11 November 2007

    Google Scholar 

  79. Schmidt SE, Hansen J, Zimmermann H, Hammersh D, Toft E, Struijk JJ (2011) Coronary artery disease and low frequency heart sound signatures. In: Computing in Cardiology, Hangzhou, China, 18–21 September 2011

    Google Scholar 

  80. Huiying L, Sakari L, Iiro H (1997) A heart sound segmentation algorithm using wavelet decomposition and reconstruction. In: 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 30 October–2 November 1997

    Google Scholar 

  81. Harsola A, Thale S, Panse M (2011) Low cost digital stethoscope for heart sounds. In: Proceedings of the International Conference & Workshop on Emerging Trends in Technology, Mumbai, India, 25–26 February 2011

    Google Scholar 

  82. Herzig J, Bickel A, Eitan A, Intrator N (2015) Monitoring cardiac stress using features extracted from S1 heart sounds. IEEE Trans Biomed Eng 62(4):1169–1178

    Article  Google Scholar 

  83. Ahlstrom C, Hult P, Rask P, Karlsson J-E, Nylander E, Dahlström U, Ask P (2006) Feature extraction for systolic heart murmur classification. Ann Biomed Eng 34(11):1666–1677

    Article  Google Scholar 

  84. Jabbari S, Ghassemian H (2011) Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders. Comput Biol Med 41(9):802–811

    Article  Google Scholar 

  85. Maglogiannis I, Loukis E, Zafiropoulos E, Stasis A (2009) Support vectors machine-based identification of heart valve diseases using heart sounds. Comput Methods Programs Biomed 95(1):47–61

    Article  Google Scholar 

  86. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society Publishing, UK

    Article  MathSciNet  MATH  Google Scholar 

  87. Sun S (2015) An innovative intelligent system based on automatic diagnostic feature extraction for diagnosing heart diseases. Knowl-Based Syst 75:224–238

    Article  Google Scholar 

  88. Tseng Y-L, Ko P-Y, Jaw F-S (2012) Detection of the third and fourth heart sounds using Hilbert-Huang transform. Biomed Eng Online 11:8. https://doi.org/10.1186/1475-925X-11-8

    Article  Google Scholar 

  89. Kumar D, Carvalho P, Antunes M, Henriques J, e Melo AS, Habetha J (2008) Heart murmur recognition and segmentation by complexity signatures. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, 20–25 August 2008

    Google Scholar 

  90. Yuenyong S, Nishihara A, Kongprawechnon W, Tungpimolrut K (2011) A framework for automatic heart sound analysis without segmentation. Biomed Eng Online 10:13. https://doi.org/10.1186/1475-925X-10-13

    Article  Google Scholar 

  91. Deng S-W, Han J-Q (2016) Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Future Gener Comput Syst 60:13–21

    Article  Google Scholar 

  92. Banerjee R, Choudhury AD, Deshpande P, Bhattacharya S, Pal A, Mandana K (2017) A robust dataset-agnostic heart disease classifier from Phonocardiogram. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, 11–15 July 2017

    Google Scholar 

  93. iHealth Labs (2018) iHealth Sense. https://ihealthlabs.com/blood-pressure-monitors/wireless-blood-pressure-wrist-monitor/. Accessed Mar 2018

  94. McElhearn K (2014) Withings wireless blood pressure monitor review: HealthKit compatibility doesn’t add much. https://www.macworld.com/article/2851095/withings-wireless-blood-pressure-monitor-review-healthkit-compatibility-doesnt-add-much.html. Accessed Mar 2018

  95. Balestrieri E, Rapuano S (2009) Advances in biomedical sensing, measurements, instrumentation and systems. In: Lay-Ekuakille A, Mukhopadhyay SC (eds) Lecture notes in electrical engineering, vol 55. Springer, Heidelberg

    Google Scholar 

  96. Haridy R (2017) Pocket-sized, affordably-priced ultrasound connects to an iPhone. https://newatlas.com/butterfly-iq-smartphone-ultrasound/51962/. Accessed Dec 2017

  97. MobiSante (2018) MobiSante smartphone ultrasound: the MobiUS SP1 system. http://www.mobisante.com/products/product-overview/. Accessed Mar 2018

  98. Philips (2018) Lumify exceptional portable ultrasound from your smart device. https://www.lumify.philips.com/web/. Accessed Mar 2018

  99. Butterfly Network (2018) Butterfly meet iQ. https://www.butterflynetwork.com/. Accessed Mar 2018

  100. Clarius (2018) Wireless portable ultrasound. https://www.clarius.com/. Accessed Mar 2018

  101. PillCam (2018) PillCam SB capsule. https://www.pillcamcrohns.com/. Accessed Mar 2018

  102. Medtronic (2018) SmartPill motility testing system. http://www.medtronic.com/covidien/en-us/products/motility-testing/smartpill-motility-testing-system.html. Accessed Mar 2018

  103. Nakashima H, Aghajan H, Augusto JC (eds) (2009) Handbook of ambient intelligence and smart environments. Springer, US

    Google Scholar 

  104. Kim K-S, Yoon T-H, Lee J-W, Kim D-J (2009) Interactive toothbrushing education by a smart toothbrush system via 3D visualization. Comput Methods Programs Biomed 96(2):125–132

    Article  Google Scholar 

  105. Marcon M, Sarti A, Tubaro S (2016) Toothbrush motion analysis to help children learn proper tooth brushing. Comput Vis Image Underst 148:34–45

    Article  Google Scholar 

  106. Marcon M, Sarti A, Tubaro S (2016) Smart toothbrushes: inertial measurement sensors fusion with visual tracking. In: European Conference on Computer Vision, Amsterdam, Netherlands, 8–16 October 2016

    Google Scholar 

  107. Kolibree (2014) Kolibree toothbrush. https://www.kolibree.com/en/. Accessed Dec 2017

  108. Oral-B (2017) Pro 5000 with bluetooth connectivity electric rechargeable toothbrush. https://oralb.com/en-us/products/pro-5000-electric-toothbrush-with-smartguide-bluetooth. Accessed Dec 2017

  109. Prophix (2017) Prophix smart toothbrush by onvi. https://www.getprophix.com/. Accessed Dec 2017

  110. PlayBrush (2017) PlayBrush—your smart toothbrush. https://www.playbrush.com/en/. Accessed Dec 2017

  111. Philips Sonicare (2017) FlexCare platinum connected. https://www.usa.philips.com/c-m-pe/electric-toothbrushes/flexcare-platinum/connected. Accessed Dec 2017

  112. BleepBleeps (2017) Benjamin brush smart music toothbrush. https://bleepbleeps.com/pages/benjamin-brush-smart-music-toothbrush. Accessed Dec 2017

  113. Bacquet and Riemenschneider (2017) Next Generation IoT Platforms. In: Vermesan O, Bacquet J (eds) Cognitive hyperconnected digital transformation: internet of things intelligence evolution. River Publishers, Denmark

    Google Scholar 

  114. Balakrishnan G, Durand F, Guttag J (2013) Detecting pulse from head motions in video. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 23–28 June 2013

    Google Scholar 

  115. Shan L, Yu M (2013) Video-based heart rate measurement using head motion tracking and ICA. In: 6th International Congress on Image and Signal Processing, Hangzhou, China, 16–18 December 2013

    Google Scholar 

  116. Irani R, Nasrollahi K, Moeslund TB (2014) Improved pulse detection from head motions using DCT. In: International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 5–8 January 2014

    Google Scholar 

  117. Sikdar A, Behera SK, Dogra DP (2016) Computer-vision-guided human pulse rate estimation: a review. IEEE Rev Biomed Eng 9:91–105

    Article  Google Scholar 

  118. Hassan M, Malik A, Fofi D, Saad N, Karasfi B, Ali Y, Meriaudeau F (2017) Heart rate estimation using facial video: a review. Biomed Signal Process Control 38:346–360

    Article  Google Scholar 

  119. Wieringa FP, Mastik F, van der Steen AF (2005) Contactless multiple wavelength photoplethysmographic imaging: a first step toward “SpO2 camera” technology. Ann Biomed Eng 33(8):1034–1041

    Article  Google Scholar 

  120. Stankevich E, Paramonov I, Timofeev I (2012) Mobile phone sensors in health applications. In: 12th Conference of Open Innovations Association, Oulu, Finland, 5–9 November 2012

    Google Scholar 

  121. Pawankiattikun V, Kondo T (2014) A method for contact-free heart rate measurement on a video sequence using simulink. In: 7th Biomedical Engineering International Conference, Fukuoka, Japan, 26–28 November 2014

    Google Scholar 

  122. Pursche T, Krajewski J, Moeller R (2012) Video-based heart rate measurement from human faces. In: IEEE International Conference on Consumer Electronics, Las Vegas, USA, 13–16 January 2012

    Google Scholar 

  123. Lee K-Z, Hung P-C, Tsai L-W (2012) Contact-free heart rate measurement using a camera. In: 9th Conference on Computer and Robot Vision, Toronto, Canada, 28–30 November 2012

    Google Scholar 

  124. Verkruysse W, Svaasand LO, Nelson JS (2008) Remote plethysmographic imaging using ambient light. Opt Express 16(26):21434–21445

    Article  Google Scholar 

  125. Jonathan E, Leahy M (2010) Investigating a smartphone imaging unit for photoplethysmography. Physiol Meas 31(11):N79–N83

    Article  Google Scholar 

  126. Jonathan E, Leahy MJ (2011) Cellular phone-based photoplethysmographic imaging. J Biophotonics 4(5):293–296

    Article  Google Scholar 

  127. Kwon S, Kim H, Park KS (2012) Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, USA, 28 August–1 September 2012

    Google Scholar 

  128. Al-Naji A, Perera AG, Chahl J (2017) Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle. Biomed Eng Online 16(1):101. https://doi.org/10.1186/s12938-017-0395-y

    Article  Google Scholar 

  129. Li X, Chen J, Zhao G, Pietikainen M (2014) Remote heart rate measurement from face videos under realistic situations. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 23–28 June 2014

    Google Scholar 

  130. Kumar M, Veeraraghavan A, Sabharwal A (2015) DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed Opt Express 6(5):1565–1588

    Article  Google Scholar 

  131. Huelsbusch M (2008) An image-based functional method for opto-electronic detection of skin-perfusion. Dissertation, RWTH Aachen University

    Google Scholar 

  132. Lewandowska M, Rumiński J, Kocejko T, Nowak J (2011) Measuring pulse rate with a webcam—a non-contact method for evaluating cardiac activity. In: Federated Conference on Computer Science and Information Systems, Szczecin, Poland, 18–21 September 2011

    Google Scholar 

  133. Poh M-Z, McDuff DJ, Picard RW (2011) Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans Biomed Eng 58(1):7–11

    Article  Google Scholar 

  134. Poh M-Z, McDuff DJ, Picard RW (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express 18(10):10762–10774

    Article  Google Scholar 

  135. Rouast PV, Adam MT, Chiong R, Cornforth D, Lux E (2017) Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front Comput Sci. https://doi.org/10.1007/s11704-016-6243-6

    Article  Google Scholar 

  136. Yu Y-P, Kwan B-H, Lim C-L, Wong S-L, Raveendran P (2013) Video-based heart rate measurement using short-time Fourier transform. In: International Symposium on Intelligent Signal Processing and Communications Systems, Naha, Japan, 12–15 November 2013

    Google Scholar 

  137. Wei L, Tian Y, Wang Y, Ebrahimi T, Huang T (2012) Automatic webcam-based human heart rate measurements using laplacian eigenmap. In: Lee KM, Matsushita Y, Rehg JM, Hu Z (eds) Computer Vision—ACCV 2012. ACCV 2012. Lecture notes in computer science, vol 7725. Springer, Heidelberg

    Google Scholar 

  138. Li M-C, Lin Y-H (2015) A real-time non-contact pulse rate detector based on smartphone. In: International Symposium on Next-Generation Electronics, Taipei, Taiwan, 4–6 May 2015

    Google Scholar 

  139. McDuff D, Gontarek S, Picard RW (2014) Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Trans Biomed Eng 61(10):2593–2601

    Article  Google Scholar 

  140. Lam A, Kuno Y (2015) Robust heart rate measurement from video using select random patches. In: IEEE International Conference on Computer Vision, Santiago, Chile, 7–13 December 2015

    Google Scholar 

  141. Wu H-Y (2012) Eulerian video processing and medical applications. Dissertation, Massachusetts Institute of Technology

    Google Scholar 

  142. Datcu D, Cidota M, Lukosch S, Rothkrantz L (2013) Noncontact automatic heart rate analysis in visible spectrum by specific face regions. In: Proceedings of the 14th International Conference on Computer Systems and Technologies, Ruse, Bulgaria, 28–29 June 2013

    Google Scholar 

  143. Fallet S, Moser V, Braun F, Vesin J-M (2016) Imaging photoplethysmography: what are the best locations on the face to estimate heart rate? In: Computing in Cardiology Conference, Vancouver, Canada, 11–14 September 2016

    Google Scholar 

  144. Blackford EB, Estepp JR, Piasecki AM, Bowers MA, Klosterman SL (2016) Long-range non-contact imaging photoplethysmography: cardiac pulse wave sensing at a distance. In: Optical Diagnostics and Sensing XVI: Toward Point-of-Care Diagnostics, San Francisco, USA, 13–18 February 2016

    Google Scholar 

  145. Sun Y, Papin C, Azorin-Peris V, Kalawsky R, Greenwald S, Hu S (2012) Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam. J Biomed Opt 17(3):0370051–03700510

    Google Scholar 

  146. Kong L, Zhao Y, Dong L, Jian Y, Jin X, Li B, Feng Y, Liu M, Liu X, Wu H (2013) Non-contact detection of oxygen saturation based on visible light imaging device using ambient light. Opt Express 21(15):17464–17471

    Article  Google Scholar 

  147. Feng L, Po L-M, Xu X, Li Y, Ma R (2015) Motion-resistant remote imaging photoplethysmography based on the optical properties of skin. IEEE Trans Circuits Syst Video Technol 25(5):879–891

    Article  Google Scholar 

  148. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: 12th European Conference on Computer Vision, Florence, Italy, 7–13 October 2012

    Google Scholar 

  149. Haan Gd, Jeanne V (2013) Robust pulse rate from chrominance-based rPPG. IEEE Trans Biomed Eng 60(10):2878–2886

    Article  Google Scholar 

  150. Wang W, Stuijk S, De Haan G (2015) Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Trans Biomed Eng 62(2):415–425

    Article  Google Scholar 

  151. Haan Gd, Van Leest A (2014) Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol Meas 35(9):1913

    Google Scholar 

  152. Wang W, Stuijk S, De Haan G (2016) A novel algorithm for remote photoplethysmography: spatial subspace rotation. IEEE Trans Biomed Eng 63(9):1974–1984

    Article  Google Scholar 

  153. Fan X (2017) Scalable teaching and learning via intelligent user interfaces. Dissertation, University of Pittsburgh

    Google Scholar 

  154. Arandjelovic O (2012) Gradient edge map features for frontal face recognition under extreme illumination changes. In: 23rd British Machine Vision Conference, Surrey, UK, 3–7 September 2012

    Google Scholar 

  155. Bal U (2015) Non-contact estimation of heart rate and oxygen saturation using ambient light. Biomed Opt Express 6(1):86–97

    Article  Google Scholar 

  156. McDuff D, Gontarek S, Picard RW (2014) Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera. IEEE Trans Biomed Eng 61(12):2948–2954

    Article  Google Scholar 

  157. Xu S, Sun L, Rohde GK (2014) Robust efficient estimation of heart rate pulse from video. Biomed Opt Express 5(4):1124–1135

    Article  Google Scholar 

  158. Scully CG, Lee J, Meyer J, Gorbach AM, Granquist-Fraser D, Mendelson Y, Chon KH (2012) Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng 59(2):303–306

    Article  Google Scholar 

  159. Chong JW, Esa N, McManus DD, Chon KH (2015) Arrhythmia discrimination using a smart phone. IEEE J Biomed Health Inform 19(3):815–824

    Google Scholar 

  160. McManus DD, Lee J, Maitas O, Esa N, Pidikiti R, Carlucci A, Harrington J, Mick E, Chon KH (2013) A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation. Heart Rhythm 10(3):315–319

    Article  Google Scholar 

  161. Lee J, Reyes BA, McManus DD, Maitas O, Chon KH (2013) Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng 60(1):203–206

    Article  Google Scholar 

  162. Huang S-C, Hung P-H, Hong C-H, Wang H-M (2014) A new image blood pressure sensor based on PPG, RRT, BPTT, and harmonic balancing. IEEE Sens J 14(10):3685–3692

    Article  Google Scholar 

  163. Rea MS (ed) (2000) The IESNA lighting handbook: reference and application. Illuminating Engineering Society of North America, USA

    Google Scholar 

  164. Tarassenko L, Villarroel M, Guazzi A, Jorge J, Clifton D, Pugh C (2014) Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiol Meas 35(5):807–813

    Article  Google Scholar 

  165. Belhumeur PN, Kriegman DJ (1998) What is the set of images of an object under all possible illumination conditions? Int J Comput Vision 28(3):245–260

    Article  Google Scholar 

  166. Georghiades AS, Belhumeur PN, Kriegman DJ (2000) From few to many: generative models for recognition under variable pose and illumination. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 28–30 March 2000

    Google Scholar 

  167. Riklin-Raviv T, Shashua A (1999) The quotient image: class based re-rendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Google Scholar 

  168. Blanz V, Romdhani S, Vetter T (2002) Face identification across different poses and illuminations with a 3d morphable model. In: 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, USA, 21 May 2002

    Google Scholar 

  169. Chen D-Y, Wang J-J, Lin K-Y, Chang H-H, Wu H-K, Chen Y-S, Lee S-Y (2015) Image sensor-based heart rate evaluation from face reflectance using Hilbert-Huang transform. IEEE Sens J 15(1):618–627

    Article  Google Scholar 

  170. Lee D, Kim J, Kwon S, Park K (2015) Heart rate estimation from facial photoplethysmography during dynamic illuminance changes. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 25–29 August 2015

    Google Scholar 

  171. Cheng J, Chen X, Xu L, Wang ZJ (2016) Illumination variation-resistant video-based heart rate measurement using joint blind source separation and ensemble empirical mode decomposition. IEEE J Biomed Health Inform 21(5):1422–1433

    Article  Google Scholar 

  172. Blackford EB, Estepp JR (2015) Effects of frame rate and image resolution on pulse rate measured using multiple camera imaging photoplethysmography. In: Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, Orlando, USA, 21–26 February 2015

    Google Scholar 

  173. McDuff DJ, Blackford EB, Estepp JR (2017) The impact of video compression on remote cardiac pulse measurement using imaging photoplethysmography. In: 12th IEEE International Conference on Automatic Face & Gesture Recognition, Washington, USA, 30 May–3 June 2017

    Google Scholar 

  174. Wang W, den Brinker AC, Stuijk S, de Haan G (2017) Algorithmic principles of remote PPG. IEEE Trans Biomed Eng 64(7):1479–1491

    Article  Google Scholar 

  175. McDuff DJ, Estepp JR, Piasecki AM, Blackford EB (2015) A survey of remote optical photoplethysmographic imaging methods. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 25–29 August 2015

    Google Scholar 

  176. Daw W, Kingshott R, Saatchi R, Burke D, Holloway A, Travis J, Evans R, Jones A, Hughes B, Elphick H (2016) Medical devices for measuring respiratory rate in children. J Adv Biomed Eng Technol 3:21–27

    Article  Google Scholar 

  177. Everett JS, Budescu M, Sommers MS (2012) Making sense of skin color in clinical care. Clin Nurs Res 21(4):495–516

    Article  Google Scholar 

  178. O’Donnell AT, Kim CC (2012) Update and clinical use of imaging technologies for pigmented lesions of the skin. Semin Cutan Med Surg 31(1):38–44

    Article  Google Scholar 

  179. Rathore S, Kower M, Kumar TS (2013) Colour and contrast enhancement for improved skin lesion segmentation using retinex theory. IJERT 2(7):2450–2456

    Google Scholar 

  180. Witmer WK, Lebovitz PJ (2012) Clinical photography in the dermatology practice. Semin Cutan Med Surg 31(3):191–199

    Article  Google Scholar 

  181. Khalili Moghaddam G (2016) Quantitative measurements on holographic grating sensors for point-of-care diagnostics. Dissertation, University of Cambridge

    Google Scholar 

  182. Garg SJ (2016) Applicability of smartphone-based screening programs. JAMA ophthalmol 134(2):158–159

    Article  Google Scholar 

  183. Giardini ME, Livingstone IA, Jordan S, Bolster NM, Peto T, Burton M, Bastawrous A (2014) A smartphone based ophthalmoscope. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 26–30 August 2014

    Google Scholar 

  184. Bastawrous A, Leak C, Howard F, Kumar V (2012) Validation of near eye tool for refractive assessment (NETRA)—Pilot study. J Mob Technol Med 1(3):6–16

    Article  Google Scholar 

  185. Gaiser H, Moore B, Pamplona V, Solaka N, Schafran D, Merrill D, Sharpe N, Geringer J, Raskar R (2013) Comparison of a novel cell phone-based refraction technique (Netra-G) with subjective refraction. Invest Ophth Vis Sci 54(15):2340

    Google Scholar 

  186. Ciuffreda KJ, Rosenfield M (2015) Evaluation of the SVOne: a handheld, smartphone-based autorefractor. Optom Vision Sci 92(12):1133–1139

    Article  Google Scholar 

  187. Fink W, Garcia K, Tarbell M (2016) Smartphone-based head-mounted binocular high-speed pupillometer. In: Annual Meeting of the Association for Research in Vision and Ophthalmology, Seattle, USA, 1–5 May

    Google Scholar 

  188. Fink W, Tarbell M (2015) Smart ophthalmics: a smart service platform for tele-ophthalmology. Invest Ophth Vis Sci 56(7):4110

    Google Scholar 

  189. Park JG, Moon CT, Park DS, Song SW (2015) Clinical utility of an automated pupillometer in patients with acute brain lesion. J Korean Neurosurg Soc 58(4):363–367

    Article  Google Scholar 

  190. Lord RK, Shah VA, San Filippo AN, Krishna R (2010) Novel uses of smartphones in ophthalmology. Ophthalmology 117(6). https://doi.org/10.1016/j.ophtha.2010.01.001

    Article  Google Scholar 

  191. Bastawrous A (2012) Smartphone fundoscopy. Ophthalmology 119(2). https://doi.org/10.1016/j.ophtha.2011.11.014

    Article  Google Scholar 

  192. Kim DY, Delori F, Mukai S (2012) Smartphone photography safety. Ophthalmology 119(10):2200–2201

    Article  Google Scholar 

  193. Haddock LJ, Kim DY, Mukai S (2013) Simple, inexpensive technique for high-quality smartphone fundus photography in human and animal eyes. J Ophthalmol. https://doi.org/10.1155/2013/518479

    Article  Google Scholar 

  194. Jalil M, Ferenczy SR, Shields CL (2017) iPhone 4s and iPhone 5s imaging of the eye. Ocul Oncol Pathol 3(1):49–55

    Article  Google Scholar 

  195. Oluleye T (2014) Mobile phones for fundus photography in Ibadan, Sub Sahara Africa. Adv Ophthalmol Vis Syst 1(4):00020. https://doi.org/10.15406/aovs.2014.01.00020

  196. Ademola-Popoola D, Olatunji V (2017) Retinal imaging with smartphone. Niger J Clin Pract 20(3):341–345

    Article  Google Scholar 

  197. Sankaranarayanan R (2014) Screening for cancer in low-and middle-income countries. Ann Glob Health 80(5):412–417

    Article  Google Scholar 

  198. Quinley KE, Gormley RH, Ratcliffe SJ, Shih T, Szep Z, Steiner A, Ramogola-Masire D, Kovarik CL (2011) Use of mobile telemedicine for cervical cancer screening. J Telemed Telecare 17(4):203–209

    Article  Google Scholar 

  199. Ricard-Gauthier D, Wisniak A, Catarino R, van Rossum AF, Meyer-Hamme U, Negulescu R, Scaringella S, Jinoro J, Vassilakos P, Petignat P (2015) Use of smartphones as adjuvant tools for cervical cancer screening in low-resource settings. J Low Genit Dis 19(4):295–300

    Article  Google Scholar 

  200. Parham GP, Mwanahamuntu MH, Pfaendler KS, Sahasrabuddhe VV, Myung D, Mkumba G, Kapambwe S, Mwanza B, Chibwesha C, Hicks ML (2010) eC3—a modern telecommunications matrix for cervical cancer prevention in Zambia. J Low Genit Dis 14(3). https://doi.org/10.1097/LGT.0b013e3181cd6d5e

    Article  Google Scholar 

  201. Gallay C, Girardet A, Viviano M, Catarino R, Benski A-C, Tran PL, Ecabert C, Thiran J-P, Vassilakos P, Petignat P (2017) Cervical cancer screening in low-resource settings: a smartphone image application as an alternative to colposcopy. Int J Womens Health 9:455–461

    Article  Google Scholar 

  202. FLIR® Systems (2017) FLIR ONE® PRO. http://www.flir.com/flirone/pro/. Accessed Dec 2017

  203. Seal A, Bhattacharjee D, Naripuri M (2013) Thermal human face recognition for biometric security system. In: Srivastava R (ed) Research developments in biometrics and video processing techniques. IGI Global, USA

    Google Scholar 

  204. Cardone D, Pinti P, Merla A (2015) Thermal infrared imaging-based computational psychophysiology for psychometrics. Comput Math Method Med. https://doi.org/10.1155/2015/984353

    Article  Google Scholar 

  205. Garbey M, Sun N, Merla A, Pavlidis I (2007) Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans Biomed Eng 54(8):1418–1426

    Article  Google Scholar 

  206. Sun N, Pavlidis I, Garbey M, Fei J (2006) Harvesting the thermal cardiac pulse signal. In: 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, Copenhagen, Denmark, 1–6 October 2006

    Google Scholar 

  207. Bourlai T, Buddharaju P, Pavlidis I, Bass B (2009) On enhancing cardiac pulse measurements through thermal imaging. In: 9th International Conference on Information Technology and Applications in Biomedicine, Larnaca, Cyprus, 4–7 November 2009

    Google Scholar 

  208. Yang M, Liu Q, Turner T, Wu Y (2008) Vital sign estimation from passive thermal video. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 23–28 June 2008

    Google Scholar 

  209. Zhou Y, Tsiamyrtzis P, Lindner P, Timofeyev I, Pavlidis I (2013) Spatiotemporal smoothing as a basis for facial tissue tracking in thermal imaging. IEEE Trans Biomed Eng 60(5):1280–1289

    Article  Google Scholar 

  210. Chekmenev SY, Farag AA, Essock EA (2007) Thermal imaging of the superficial temporal artery: an arterial pulse recovery model. In: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 17–22 June 2007

    Google Scholar 

  211. Gault TR, Blumenthal N, Farag AA, Starr T (2010) Extraction of the superficial facial vasculature, vital signs waveforms and rates using thermal imaging. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition—Workshops, San Francisco, USA, 13–18 June 2010

    Google Scholar 

  212. Gault T, Farag A (2013) A fully automatic method to extract the heart rate from thermal video. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, USA, 23–28 June 2013

    Google Scholar 

  213. Murthy R, Pavlidis I (2006) Noncontact measurement of breathing function. IEEE Eng Med Biol Mag 25(3):57–67

    Article  Google Scholar 

  214. Fei J, Pavlidis I (2007) Virtual thermistor. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22–26 August 2007

    Google Scholar 

  215. Pereira CB, Yu X, Czaplik M, Blazek V, Venema B, Leonhardt S (2016) Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects. J Clin Monit Comput 31(6):1241–1254

    Article  Google Scholar 

  216. Pereira CB, Yu X, Czaplik M, Rossaint R, Blazek V, Leonhardt S (2015) Remote monitoring of breathing dynamics using infrared thermography. Biomed Opt Express 6(11):4378–4394

    Article  Google Scholar 

  217. Murthy JN, van Jaarsveld J, Fei J, Pavlidis I, Harrykissoon RI, Lucke JF, Faiz S, Castriotta RJ (2009) Thermal infrared imaging: a novel method to monitor airflow during polysomnography. Sleep 32(11):1521–1527

    Article  Google Scholar 

  218. Lewis GF, Gatto RG, Porges SW (2011) A novel method for extracting respiration rate and relative tidal volume from infrared thermography. Psychophysiology 48(7):877–887

    Article  Google Scholar 

  219. Jarczok MN, Kleber ME, Koenig J, Loerbroks A, Herr RM, Hoffmann K, Fischer JE, Benyamini Y, Thayer JF (2015) Investigating the associations of self-rated health: heart rate variability is more strongly associated than inflammatory and other frequently used biomarkers in a cross sectional occupational sample. PLoS ONE 10(2):e0117196. https://doi.org/10.1371/journal.pone.0117196

    Article  Google Scholar 

  220. Idler EL, Benyamini Y (1999) Community studies reporting association between self-rated health and mortality. Res Aging 21:392–401

    Article  Google Scholar 

  221. Pinquart M (2001) Correlates of subjective health in older adults: a meta-analysis. Psychol Aging 16(3):414–426

    Article  Google Scholar 

  222. Schmidt B, Loerbroks A, Herr RM, Wilson MG, Jarczok MN, Litaker D, Mauss D, Bosch JA, Fischer JE (2014) Associations between supportive leadership and employees self-rated health in an occupational sample. Int J Behav Med 21(5):750–756

    Article  Google Scholar 

  223. Fuster V (1999) Epidemic of cardiovascular disease and stroke: the three main challenges. Circulation 99(9):1132–1137

    Article  Google Scholar 

  224. Rothwell P, Coull A, Silver L, Fairhead J, Giles M, Lovelock C, Redgrave J, Bull L, Welch S, Cuthbertson F (2005) Population-based study of event-rate, incidence, case fatality, and mortality for all acute vascular events in all arterial territories (Oxford Vascular Study). Lancet 366(9499):1773–1783

    Article  Google Scholar 

  225. Thayer JF, Yamamoto SS, Brosschot JF (2010) The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol 141(2):122–131

    Article  Google Scholar 

  226. Voss A, Heitmann A, Schroeder R, Peters A, Perz S (2012) Short-term heart rate variability—age dependence in healthy subjects. Physiol Meas 33(8):1289–1311

    Article  Google Scholar 

  227. Hillebrand S, Gast KB, de Mutsert R, Swenne CA, Jukema JW, Middeldorp S, Rosendaal FR, Dekkers OM (2013) Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose–response meta-regression. Europace 15(5):742–749

    Article  Google Scholar 

  228. Thompson PD (2011) The cardiovascular risks of diving. Undersea Hyperb Med 38(4):271–277

    Google Scholar 

  229. Bove AA (2011) The cardiovascular system and diving risk. Undersea Hyperb Med 38(4):261–269

    Google Scholar 

  230. Denoble P, Caruso J, de L Dear G, Pieper CF, Vann R (2008) Common causes of open-circuit recreational diving fatalities. Undersea Hyperb Med 35(6):393–406

    Google Scholar 

  231. Tervo T, Räty E, Sulander P, Holopainen JM, Jaakkola T, Parkkari K (2013) Sudden death at the wheel due to a disease attack. Traffic Inj Prev 14(2):138–144

    Article  Google Scholar 

  232. Petch M (1998) Driving and heart disease. Eur Heart J 19(8):1165–1177

    Article  Google Scholar 

  233. Fieselmann JF, Hendryx MS, Helms CM, Wakefield DS (1993) Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients. J Gen Intern Med 8(7):354–360

    Article  Google Scholar 

  234. Melillo P, Izzo R, Orrico A, Scala P, Attanasio M, Mirra M, De Luca N, Pecchia L (2015) Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS ONE 10(3):e0118504. https://doi.org/10.1371/journal.pone.0118504

    Article  Google Scholar 

  235. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    Article  Google Scholar 

  236. Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N (2013) The appropriate use of approximate entropy and sample entropy with short data sets. Ann Biomed Eng 41(2):349–365

    Article  Google Scholar 

  237. Ji L, Li P, Li K, Wang X, Liu C (2015) Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method. Biomed Eng Online 14:64. https://doi.org/10.1186/s12938-015-0063-z

    Article  Google Scholar 

  238. MedlinePlus (216) Coronary artery disease. MedlinePlus. https://medlineplus.gov/coronaryarterydisease.html. Accessed Dec 2017

  239. Banerjee R, Choudhury AD, Datta S, Pal A, Mandana KM (2017) Non invasive detection of coronary artery disease using PCG and PPG. In: Giokas K, Bokor L, Hopfgartner F (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham

    Google Scholar 

  240. Villarroel M, Guazzi A, Jorge J, Davis S, Watkinson P, Green G, Shenvi A, McCormick K, Tarassenko L (2014) Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthc Technol Lett 1(3):87–91

    Article  Google Scholar 

  241. Werth J, Atallah L, Andriessen P, Long X, Zwartkruis-Pelgrim E, Aarts RM (2017) Unobtrusive sleep state measurements in preterm infants—a review. Sleep Med Rev 32:109–122

    Article  Google Scholar 

  242. Soto RG, Fu ES, Vila H Jr, Miguel RV (2004) Capnography accurately detects apnea during monitored anesthesia care. Anesth Analg 99(2):379–382

    Article  Google Scholar 

  243. Berry R (2002) Esophageal and nasal pressure monitoring during sleep. In: Sateia M, Carskadon MA, Lee-Chiong TL (eds) Sleep medicine. Hanley & Belfus, USA

    Google Scholar 

  244. Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn B (2012) The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications. In: American Academy of Sleep Medicine. Available via https://aasm.org/clinical-resources/scoring-manual/. Accessed Mar 2018

  245. Bornstein SK (1982) Respiratory monitoring during sleep: polysomnography. In: Guilleminault C (ed) Sleeping and waking disorders: indications and techniques. Addison-Wesley Publishing Company, USA

    Google Scholar 

  246. Lee-Chiong TL (2003) Monitoring respiration during sleep. Clin Chest Med 24(2):297–306. https://doi.org/10.1016/S0272-5231(03)00021-2

    Article  Google Scholar 

  247. Richter DW (2003) Commentary on eupneic breathing patterns and gasping. Respir Physiol Neurobiol 139(1):121–130

    Article  MathSciNet  Google Scholar 

  248. Wilburta LQ, Pooler M, Tamparo CD, Dahl BM, Morris J (2013) Delmar‘s comprehensive medical assisting: administrative and clinical competencies. Cengage Learning, USA

    Google Scholar 

  249. White GC (2012) Basic clinical lab competencies for respiratory care: an integrated approach. Cengage Learning, USA

    Google Scholar 

  250. Tufik S, Santos-Silva R, Taddei JA, Bittencourt LRA (2010) Obstructive sleep apnea syndrome in the Sao Paulo epidemiologic sleep study. Sleep Med 11(5):441–446

    Article  Google Scholar 

  251. Heinzer R, Vat S, Marques-Vidal P, Marti-Soler H, Andries D, Tobback N, Mooser V, Preisig M, Malhotra A, Waeber G (2015) Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. Lancet Respir Med 3(4):310–318

    Article  Google Scholar 

  252. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 177(9):1006–1014

    Article  Google Scholar 

  253. Ott SR, Korostovtseva L, Schmidt M, Horvath T, Brill A-K, Bassetti CL (2017) Sleep-disordered breathing: clinical features, pathophysiology and diagnosis. Swiss Med Wkly 147:w14436. https://doi.org/10.4414/smw.2017.14436

    Article  Google Scholar 

  254. Franklin KA, Lindberg E (2015) Obstructive sleep apnea is a common disorder in the population—a review on the epidemiology of sleep apnea. J Thorac Dis 7(8):1311–1322

    Google Scholar 

  255. Ryan CM, Wilton K, Bradley TD, Alshaer H (2017) In-hospital diagnosis of sleep apnea in stroke patients using a portable acoustic device. Sleep Breath 21(2):453–460

    Article  Google Scholar 

  256. Johnson KG, Johnson DC (2010) Frequency of sleep apnea in stroke and TIA patients: a meta-analysis. J Clin Sleep Med 6(2):131–137

    Google Scholar 

  257. Hermann DM, Siccoli M, Kirov P, Gugger M, Bassetti CL (2007) Central periodic breathing during sleep in acute ischemic stroke. Stroke 38(3):1082–1084

    Article  Google Scholar 

  258. Nopmaneejumruslers C, Kaneko Y, Hajek V, Zivanovic V, Bradley TD (2005) Cheyne-Stokes respiration in stroke: relationship to hypocapnia and occult cardiac dysfunction. Am J Respir Crit Care Med 171(9):1048–1052

    Article  Google Scholar 

  259. Alshaer H, Levchenko A, Bradley TD, Pong S, Tseng W-H, Fernie GR (2013) A system for portable sleep apnea diagnosis using an embedded data capturing module. J Clin Monit Comput 27(3):303–311

    Article  Google Scholar 

  260. Masa JF, Duran-Cantolla J, Capote F, Cabello M, Abad J, Garcia-Rio F, Ferrer A, Mayos M, Gonzalez-Mangado N, de la Peña M (2014) Effectiveness of home single-channel nasal pressure for sleep apnea diagnosis. Sleep 37(12):1953–1961

    Article  Google Scholar 

  261. Guilleminault C, Peraita R, Souquet M, Dement WC (1975) Apneas during sleep in infants: possible relationship with sudden infant death syndrome. Science 190(4215):677–679

    Article  Google Scholar 

  262. Alekhin M, Anishchenko L, Zhuravlev A, Ivashov S, Korostovtseva L, Sviryaev Y, Konradi A, Parashin V, Bogomolov A (2013) Estimation of information value of diagnostic data obtained by bioradiolocation pneumography in non-contact screening of sleep apnea syndrome. Biomed Eng. https://doi.org/10.1007/s10527-013-9343-8

    Article  Google Scholar 

  263. Isidoro SI, Salvaggio A, Bue AL, Romano S, Marrone O, Insalaco G (2015) Effect of obstructive sleep apnea diagnosis on health related quality of life. Health Qual Life Outcomes 13:68. https://doi.org/10.1186/s12955-015-0253-1

    Article  Google Scholar 

  264. Stewart SA, Skomro R, Reid J, Penz E, Fenton M, Gjevre J, Cotton D (2015) improvement in obstructive sleep apnea diagnosis and management wait times: a retrospective analysis of a home management pathway for obstructive sleep apnea. Can Respir J 22(3):167–170

    Article  Google Scholar 

  265. Kim RD, Kapur VK, Redline-Bruch J, Rueschman M, Auckley DH, Benca RM, Foldvary-Schafer NR, Iber C, Zee PC, Rosen CL (2015) An economic evaluation of home versus laboratory-based diagnosis of obstructive sleep apnea. Sleep 38(7):1027–1037

    Article  Google Scholar 

  266. Young T, Palta M, Dempsey J, Peppard PE, Nieto FJ, Hla KM (2009) Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study. WMJ 108(5):246–249

    Google Scholar 

  267. Lorenzi-Filho G, Genta P, Drager L (2017) Are we missing obstructive sleep apnea diagnosis? Rev Port Pneumol 23(2):55–56

    Google Scholar 

  268. Taplidou SA, Hadjileontiadis LJ (2007) Wheeze detection based on time-frequency analysis of breath sounds. Comput Biol Med 37(8):1073–1083

    Article  Google Scholar 

  269. Brooks D, Thomas J (1995) Interrater reliability of auscultation of breath sounds among physical therapists. Phys Ther 75(12):1082–1088

    Article  Google Scholar 

  270. Spiteri M, Cook D, Clarke S (1988) Reliability of eliciting physical signs in examination of the chest. Lancet 331(8590):873–875

    Article  Google Scholar 

  271. Prodhan P, Rosa RSD, Shubina M, Haver KE, Matthews BD, Buck S, Kacmarek RM, Noviski NN (2008) Wheeze detection in the pediatric intensive care unit: comparison among physician, nurses, respiratory therapists, and a computerized respiratory sound monitor. Respir Care 53(10):1304–1309

    Google Scholar 

  272. Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N (2004) Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34(6):523–537

    Article  Google Scholar 

  273. Sengupta N, Sahidullah M, Saha G (2016) Lung sound classification using cepstral-based statistical features. Comput Biol Med 75:118–129

    Article  Google Scholar 

  274. Abbas A, Fahim A (2010) An automated computerized auscultation and diagnostic system for pulmonary diseases. J Med Syst 34(6):1149–1155

    Article  Google Scholar 

  275. Yamashita M, Matsunaga S, Miyahara S (2011) Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 22–27 May 2011

    Google Scholar 

  276. Datta S, Choudhury AD, Deshpande P, Bhattacharya S, Pal A (2017) Automated lung sound analysis for detecting pulmonary abnormalities. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, 11–15 July 2017

    Google Scholar 

  277. Kevat AC, Kalirajah A, Roseby R (2017) Digital stethoscopes compared to standard auscultation for detecting abnormal paediatric breath sounds. Eur J Pediatr 176(7):989–992

    Article  Google Scholar 

  278. Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR (2016) Application of semi-supervised deep learning to lung sound analysis. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, USA, 16–20 August 2016

    Google Scholar 

  279. Marciniuk D, Ferkol T, Nana A, de Oca MM, Rabe K, Billo N, Zar H (2014) Respiratory diseases in the world. Realities of today—opportunities for tomorrow. In: Afr J Respir Med. Available via: https://pdfs.semanticscholar.org/df52/2102c7d0e3334093c394be1e668d71747221.pdf. Accessed Mar 2018

  280. Gruffydd-Jones K, Nicholson I, Best L, Connell E (1999) Why don’t patients attend the asthma clinic? Prim Care Resp J 7:36–38

    Google Scholar 

  281. Royal College of Physicians (2015) Why asthma still kills: The National Review of Asthma Deaths (NRAD) confidential enquiry report. London, RCP, 2014. www.rcplondon.ac.uk/sites/default/files/why-asthma-still-kills-full-report.pdf. Accessed Mar 2018

  282. Pinnock H, Slack R, Pagliari C, Price D, Sheikh A (2007) Understanding the potential role of mobile phone-based monitoring on asthma self-management: qualitative study. Clin Exp Allergy 37(5):794–802

    Article  Google Scholar 

  283. Mohammadi D (2018) Smart inhalers: will they help to improve asthma care? Pharm J—A Royal Pharmaceutical Society Publication. Available via: https://www.pharmaceutical-journal.com/news-and-analysis/features/smart-inhalers-will-they-help-to-improve-asthma-care/20202556.article. Accessed Mar 2018

  284. Thuemmler C, Bai C (2017) Health 4.0: application of industry 4.0 design principles in future asthma management. In: Health 4.0: how virtualization and big data are revolutionizing healthcare. Springer

    Google Scholar 

  285. Son J, Brennan PF, Zhou S (2016) Rescue inhaler usage prediction in smart asthma management systems using joint mixed effects logistic regression model. IIE Trans 48(4):333–346

    Article  Google Scholar 

  286. Heaney LG, McGarvey LP (2017) Personalised medicine for asthma and chronic obstructive pulmonary disease. Respiration 93(3):153–161

    Article  Google Scholar 

  287. Wiecha JM, Adams WG, Rybin D, Rizzodepaoli M, Keller J, Clay JM (2015) Evaluation of a web-based asthma self-management system: a randomised controlled pilot trial. BMC Pulm Med 15:17. https://doi.org/10.1186/s12890-015-0007-1

  288. Burbank AJ, Lewis SD, Hewes M, Schellhase DE, Rettiganti M, Hall-Barrow J, Bylander LA, Brown RH, Perry TT (2015) Mobile-based asthma action plans for adolescents. J Asthma 52(6):583–586

    Article  Google Scholar 

  289. Thomson J, Hass C, Horn I, Kleine E, Mitchell S, Gary K, Ahmed I, Hamel D, Amresh A (2017) Aspira: employing a serious game in an mHealth app to improve asthma outcomes. In: IEEE 5th International Conference on Serious Games and Applications for Health, Perth, Australia, 2–4 April 2017

    Google Scholar 

  290. Al-Dowaihi D, Al-Ajlan M, Al-Zahrani N, Al-Quwayfili N, al-Jwiser N, Kanjo E (2013) Mbreath: asthma monitoring system on the go. In: International Conference on Computer Medical Applications, Sousse, Tunisia, 20–22 January 2013

    Google Scholar 

  291. Negar N (2015) Towards mHealth solutions for asthma patients. Dissertation, Marquette University

    Google Scholar 

  292. WHO WH (1996) The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. http://apps.who.int/iris/bitstream/handle/10665/41864/0965546608_eng.pdf;jsessionid=47B0730D84595AFC9A512D602E51E10E?sequence=1. Accessed Mar 2018

  293. Kessler RC, Greenberg PE (2002) The economic burden of anxiety and stress disorders. In: Davis KL, American College of Neurophsychopharmacology (eds) Neuropsychopharmacology: the fifth generation of progress. Lippincott Williams & Wilkins, Pennsylvania

    Google Scholar 

  294. Yerkes RM, Dodson J (1968) The relation of strength of stimulus to rapidity of habit-formation. In: Punishment: Issues and experiments. J Comp Neurol Psychol. https://doi.org/10.1002/cne.920180503

    Article  Google Scholar 

  295. Cohen S (1980) Aftereffects of stress on human performance and social behavior: a review of research and theory. Psychol Bull 88(1):82–108

    Article  Google Scholar 

  296. Schuler RS (1980) Definition and conceptualization of stress in organizations. Organ Behav Hum Perform 25(2):184–215

    Article  Google Scholar 

  297. Kalia M (2002) Assessing the economic impact of stress—the modern day hidden epidemic. Metabolism 51(6 Suppl 1):49–53

    Article  Google Scholar 

  298. WHO (2008) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/. Accessed Mar 2018

  299. Arnsten AF (2015) Stress weakens prefrontal networks: molecular insults to higher cognition. Nat Neurosci 18(10):1376–1385

    Article  Google Scholar 

  300. Rowden P, Matthews G, Watson B, Biggs H (2011) The relative impact of work-related stress, life stress and driving environment stress on driving outcomes. Accid Anal Prev 43(4):1332–1340

    Article  Google Scholar 

  301. Ge Y, Qu W, Jiang C, Du F, Sun X, Zhang K (2014) The effect of stress and personality on dangerous driving behavior among Chinese drivers. Accid Anal Prev 73:34–40

    Article  Google Scholar 

  302. Hill JD, Boyle LN (2007) Driver stress as influenced by driving maneuvers and roadway conditions. Transp Res Part F Traffic Psychol Behav 10(3):177–186

    Article  Google Scholar 

  303. The American Institute of Stress (2011) Workplace stress. https://www.stress.org/workplace-stress/. Accessed Mar 2018

  304. Pavlidis I, Dowdall J, Sun N, Puri C, Fei J, Garbey M (2007) Interacting with human physiology. Comput Vis Image Underst 108(1):150–170

    Article  Google Scholar 

  305. Engert V, Merla A, Grant JA, Cardone D, Tusche A, Singer T (2014) Exploring the use of thermal infrared imaging in human stress research. PLoS ONE 9(3):e90782

    Article  Google Scholar 

  306. Cardone D, Merla A (2017) New frontiers for applications of thermal infrared imaging devices: computational psychopshysiology in the neurosciences. Sensors 17(5):1042. https://doi.org/10.3390/s17051042

  307. Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD (2012) A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 36(2):747–756

    Article  Google Scholar 

  308. Motomura N, Sakurai A, Yotsuya Y (2001) Reduction of mental stress with lavender odorant. Percept Motor Skill 93(3):713–718

    Article  Google Scholar 

  309. Tillotson J eScent®. Sensory Design & Technology Ltd. http://www.escent.ai/. Accessed Nov 2017

  310. Byrom B (2015) Brain monitoring devices in clinical trials. Appl Clin Trials. http://www.appliedclinicaltrialsonline.com/brain-monitoring-devices-clinical-trials. Accessed Mar 2018

  311. Poltavski DV (2015) The use of single-electrode wireless EEG in biobehavioral investigations. In: Rasooly A, Herold K (eds) Mobile health technologies. Methods in molecular biology, vol 1256. Humana Press, New York

    Google Scholar 

  312. Rodriguez Ortega A, Rey Solaz B, Raya A, Luis M (2013) Validation of a low-cost EEG device for mood induction studies. Stud Health Technol Inform 191:43–47

    Google Scholar 

  313. Ring E, Collins A, Bacon P, Cosh J (1974) Quantitation of thermography in arthritis using multi-isothermal analysis. II. Effect of nonsteroidal anti-inflammatory therapy on the thermographic index. Ann Rheum Dis 33(4):353–356

    Article  Google Scholar 

  314. Collins A, Ring E, Cosh J, Bacon P (1974) Quantitation of thermography in arthritis using multi-isothermal analysis. I. The thermographic index. Ann Rheum Dis 33(2):113–115

    Article  Google Scholar 

  315. Bacon P, Ring E, Collins A (1977) Thermography in the assessment of anti-rheumatic agents. In: Gordon JL, Hazleman BL (eds) Rheumatoid arthritis. Elsevler, Amsterdam

    Google Scholar 

  316. Handwerker H (1990) Assessment of the effect of ibuprofen and other non-steroidal anti-rheumatic drugs in experimental algesimetry. Z Rheumatol 50(Suppl 1):15–18

    Google Scholar 

  317. Bruning RS, Dahmus JD, Kenney WL, Holowatz LA (2013) Aspirin and clopidogrel alter core temperature and skin blood flow during heat stress. Med Sci Sport Exer 45(4):674–682

    Article  Google Scholar 

  318. Hughes JH, Henry RE, Daly MJ (1984) Influence of ethanol and ambient temperature on skin blood flow. Ann Emerg Med 13(8):597–600

    Article  Google Scholar 

  319. Wolf R, Tüzün B, Tüzün Y (1999) Alcohol ingestion and the cutaneous vasculature. Clin Dermatol 17(4):395–403

    Article  Google Scholar 

  320. Ammer K, Melnizky P, Rathkolb O (2003) Skin temperature after intake of sparkling wine, still wine or sparkling water. Thermol Int 13(3):99–102

    Google Scholar 

  321. Mannara G, Salvatori G, Pizzuti G (1993) Ethyl alcohol induced skin temperature changes evaluated by thermography. Preliminary results. Boll Soc Ital Biol Sper 69(10):587–594

    Google Scholar 

  322. Morley JE (2015) Dehydration, hypernatremia, and hyponatremia. Clin Geriatr Med 31(3):389–399

    Article  Google Scholar 

  323. Stookey JD (2005) High prevalence of plasma hypertonicity among community-dwelling older adults: results from NHANES III. J Am Diet Assoc 105(8):1231–1239

    Article  Google Scholar 

  324. Frangeskou M, Lopez-Valcarcel B, Serra-Majem L (2015) Dehydration in the elderly: a review focused on economic burden. J Nutr Health Aging 19(6):619–627

    Article  Google Scholar 

  325. Pash E, Parikh N, Hashemi L (2014) Economic burden associated with hospital postadmission dehydration. Jpen J Parenter Enteral Nutr 38(2 suppl):58S–64S

    Article  Google Scholar 

  326. Hooper L, Bunn D, Jimoh FO, Fairweather-Tait SJ (2014) Water-loss dehydration and aging. Mech Ageing Dev 136:50–58

    Article  Google Scholar 

  327. Serra-Majem L (2015) Opening remarks: the burden of disease attributable to hydration in Europe. Nutr Hosp 32(2):3

    Google Scholar 

  328. Clarys P, Alewaeters K, Lambrecht R, Barel A (2000) Skin color measurements: comparison between three instruments: the Chromameter®, the DermaSpectrometer® and the Mexameter®. Skin Res Technol 6(4):230–238

    Article  Google Scholar 

  329. Konica Minolta (2015) Color measurement. http://sensing.konicaminolta.asia/applications/color-measurement/. Accessed Feb 2016

  330. Daniel LC, Heckman CJ, Kloss JD, Manne SL (2009) Comparing alternative methods of measuring skin color and damage. Cancer Causes Control 20(3):313–321

    Article  Google Scholar 

  331. Yaroslavsky I, Childs J, Altshuler GB, Zenzie HH, Cohen R (2012) Objective measurement device for melanin optical density: dosimetry for laser and ipls in aesthetic treatments. http://bramptonlaserclinic.com/pdf/skintel_technical.pdf. Accessed Dec 2017

  332. Macdonald HM, Mavroeidi A, Aucott LA, Diffey BL, Fraser WD, Ormerod AD, Reid DM (2011) Skin color change in Caucasian postmenopausal women predicts summer-winter change in 25-hydroxyvitamin D: findings from the ANSAViD cohort study. J Clin Endocr Metab 96(6):1677–1686

    Article  Google Scholar 

  333. Zvornicanin E, Zvornicanin J, Hadziefendic B (2014) The use of smart phones in ophthalmology. Acta Inform Med 22(3):206–209

    Article  Google Scholar 

  334. Bastawrous A, Cheeseman R, Kumar A (2012) iPhones for eye surgeons. Eye 26(3):343–354

    Article  Google Scholar 

  335. Lakshminarayanan V, Zelek J, McBride A (2015) “Smartphone science” in eye care and medicine. Opt Photonics News 26(1):44–51

    Article  Google Scholar 

  336. Cheng NM, Chakrabarti R, Kam JK (2014) iPhone applications for eye care professionals: a review of current capabilities and concerns. Telemed e-Health 20(4):385–387

    Article  Google Scholar 

  337. Rodríguez-Vallejo M (2016) Comment on: ‘effectiveness of a smartphone application for testing near-visual acuity’. Eye 30(6):898–899

    Article  Google Scholar 

  338. Pathipati AS, Wood EH, Lam CK, Sáles CS, Moshfeghi DM (2016) Visual acuity measured with a smartphone app is more accurate than Snellen testing by emergency department providers. Graefes Arch Clinl Exp Ophthalmol 254(6):1175–1180

    Article  Google Scholar 

  339. Tofigh S, Shortridge E, Elkeeb A, Godley B (2015) Effectiveness of a smartphone application for testing near visual acuity. Eye 29(11):1464–1468

    Article  Google Scholar 

  340. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen S-J, Dekker JM, Fletcher A, Grauslund J (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3):556–564

    Article  Google Scholar 

  341. Viswanath K, McGavin DM (2003) Diabetic retinopathy: clinical findings and management. Community Eye Health 16(46):21–24

    Google Scholar 

  342. Willis JR, Doan QV, Gleeson M, Haskova Z, Ramulu P, Morse L, Cantrell RA (2017) Vision-related functional burden of diabetic retinopathy across severity levels in the United States. JAMA Ophthalmol 135(9):926–932

    Article  Google Scholar 

  343. Vashist P, Singh S, Gupta N, Saxena R (2011) Role of early screening for diabetic retinopathy in patients with diabetes mellitus: an overview. Indian J Community Med 36(4):247–252

    Article  Google Scholar 

  344. Rajalakshmi R, Amutha A, Ranjani H, Ali MK, Unnikrishnan R, Anjana RM, Narayan KV, Mohan V (2014) Prevalence and risk factors for diabetic retinopathy in Asian Indians with young onset type 1 and type 2 diabetes. J Diabetes Complications 28(3):291–297

    Article  Google Scholar 

  345. Micheletti JM, Hendrick AM, Khan FN, Ziemer DC, Pasquel FJ (2016) Current and next generation portable screening devices for diabetic retinopathy. J Diabetes Sci Technol 10(2):295–300

    Article  Google Scholar 

  346. Optical V Pictor Plus—digital ophtalmic imager (2017) http://volk.com/pictorplus/. Accessed Dec 2017

  347. iExaminer Welch Allyn (2017) https://www.welchallyn.com. Accessed Dec 2017

  348. Pérez GM, Swart W, Munyenyembe JK, Saranchuk P (2014) Barriers to pilot mobile teleophthalmology in a rural hospital in Southern Malawi. Pan Afr Med J 19:136. https://doi.org/10.11604/pamj.2014.19.136.5196

  349. Toy BC, Myung DJ, He L, Pan CK, Chang RT, Polkinhorne A, Merrell D, Foster D, Blumenkranz MS (2016) Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Retina 36(5):1000–1008

    Article  Google Scholar 

  350. Myung D, Jais A, He L, Blumenkranz MS, Chang RT (2014) 3D printed smartphone indirect lens adapter for rapid, high quality retinal imaging. J Mob Technol Med 3(1):9–15

    Article  Google Scholar 

  351. Brackbill RM, Thorpe LE, DiGrande L, Perrin M, Sapp JH, Wu D, Campolucci S, Walker DJ, Cone J, Pulliam P (2006) Surveillance for World Trade Center disaster health effects among survivors of collapsed and damaged buildings. MMWR Surveill Summ 55(2):1–18

    Google Scholar 

  352. Mines M, Thach A, Mallonee S, Hildebrand L, Shariat S (2000) Ocular injuries sustained by survivors of the Oklahoma City bombing. Ophthalmology 107(5):837–843

    Article  Google Scholar 

  353. WHO (2012) Cervical cancer: estimated incidence, mortality and prevalence worldwide in 2012. http://globocan.iarc.fr/old/FactSheets/cancers/cervix-new.asp. Accessed Dec 2017

  354. Forman D, de Martel C, Lacey CJ, Soerjomataram I, Lortet-Tieulent J, Bruni L, Vignat J, Ferlay J, Bray F, Plummer M (2012) Global burden of human papillomavirus and related diseases. Vaccine 30:F12–F23

    Article  Google Scholar 

  355. WHO (2014) CI5PLUS: cancer incidence in five continents time trends. http://ci5.iarc.fr/CI5plus/Default.aspx. Accessed Dec 2017

  356. American Cancer Society (2017) Global burden of cancer in women: current status, trends, and interventions. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/global-cancer-facts-and-figures/global-burden-of-cancer-in-women.pdf. Accessed Mar 2018

  357. Technologies MOD reach every patient. http://www.mobileodt.com/. Accessed Dec 2017

  358. International Agency for Research on Cancer (2011) Recent evidence on cervical cancer screening in low-resource settings. http://screening.iarc.fr/doc/ACCP_cxca_screening_2011.pdf. Accessed Mar 2018

  359. Wright TC, Kuhn L (2012) Alternative approaches to cervical cancer screening for developing countries. Best Pract Res Clin Obstet Gynaecol 26(2):197–208

    Article  Google Scholar 

  360. Arbyn M, Sankaranarayanan R, Muwonge R, Keita N, Dolo A, Mbalawa CG, Nouhou H, Sakande B, Wesley R, Somanathan T (2008) Pooled analysis of the accuracy of five cervical cancer screening tests assessed in eleven studies in Africa and India. Int J Cancer 123(1):153–160

    Article  Google Scholar 

  361. Cronjé HS, Parham GP, Cooreman BF, De Beer A, Divall P, Bam RH (2003) A comparison of four screening methods for cervical neoplasia in a developing country. Am J Obstet Gynecol 188(2):395–400

    Article  Google Scholar 

  362. Stafl A (1981) Cervicography: a new method for cervical cancer detection. Am J Obstet Gynecol 139(7):815–821

    Article  Google Scholar 

  363. De Vuyst H, Claeys P, Njiru S, Muchiri L, Steyaert S, De Sutter P, Van Marck E, Bwayo J, Temmerman M (2005) Comparison of pap smear, visual inspection with acetic acid, human papillomavirus DNA-PCR testing and cervicography. Int J Gynecol Obstet 89(2):120–126

    Article  Google Scholar 

  364. Bomfim-Hyppólito S, Santana Franco E, de Matos Gomes, Meneses Franco R, Matos de Albuquerque C, Nunes G (2006) Cervicography as an adjunctive test to visual inspection with acetic acid in cervical cancer detection screening. Int J Gynecol Obstet 92(1):58–63

    Article  Google Scholar 

  365. Pretorius RG, Bao YP, Belinson JL, Burchette RJ, Smith JS, Qiao YL (2007) Inappropriate gold standard bias in cervical cancer screening studies. Int J Cancer 121(10):2218–2224

    Article  Google Scholar 

  366. Urner E, Delavy M, Catarino R, Viviano M, Meyer-Hamme U, Benski A-C, Jinoro J, Heriniainasolo JL, Undurraga M, De Vuyst H (2017) A smartphone-based approach for triage of human papillomavirus-positive sub-Saharan African women: a prospective study. JMIR mHealth uHealth 5(5):e72

    Article  Google Scholar 

  367. Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, Panaccione R, Ghosh S, Wu JC, Chan FK (2017) Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390(10114):2769–2778

    Article  Google Scholar 

  368. Dionisio PM, Gurudu SR, Leighton JA, Leontiadis GI, Fleischer DE, Hara AK, Heigh RI, Shiff AD, Sharma VK (2010) Capsule endoscopy has a significantly higher diagnostic yield in patients with suspected and established small-bowel Crohn’s disease: a meta-analysis. Am J Gastroenterol 105(6):1240–1248

    Article  Google Scholar 

  369. Mow WS, Lo SK, Targan SR, Dubinsky MC, Treyzon L, Abreu-Martin MT, Papadakis KA, Vasiliauskas EA (2004) Initial experience with wireless capsule enteroscopy in the diagnosis and management of inflammatory bowel disease. Clin Gastroenterol Hepatol 2(1):31–40

    Article  Google Scholar 

  370. Swain P, Fritscher-Ravens A (2004) Role of video endoscopy in managing small bowel disease. Gut 53(12):1866–1875

    Article  Google Scholar 

  371. Flamant M, Trang C, Maillard O, Sacher-Huvelin S, Le Rhun M, Galmiche J-P, Bourreille A (2013) The prevalence and outcome of jejunal lesions visualized by small bowel capsule endoscopy in Crohn’s disease. Inflamm Bowel Dis 19(7):1390–1396

    Article  Google Scholar 

  372. Ilangovan R, Burling D, George A, Gupta A, Marshall M, Taylor S (2012) CT enterography: review of technique and practical tips. Br J Radiol 85(1015):876–886

    Article  Google Scholar 

  373. Jaffe TA, Gaca AM, Delaney S, Yoshizumi TT, Toncheva G, Nguyen G, Frush DP (2007) Radiation doses from small-bowel follow-through and abdominopelvic MDCT in Crohn’s disease. Am J Roentgenol 189(5):1015–1022

    Article  Google Scholar 

  374. Dignass A, Van Assche G, Lindsay J, Lémann M, Söderholm J, Colombel J, Danese S, D’Hoore A, Gassull M, Gomollón F (2010) The second European evidence-based consensus on the diagnosis and management of Crohn’s disease: current management. J Crohn’s Colitis 4(1):28–62

    Article  Google Scholar 

  375. Jensen MD, Nathan T, Rafaelsen SR, Kjeldsen J (2011) Diagnostic accuracy of capsule endoscopy for small bowel Crohn’s disease is superior to that of MR enterography or CT enterography. Clin Gastroenterol Hepatol 9(2):124–129

    Article  Google Scholar 

  376. Leighton JA, Gralnek IM, Cohen SA, Toth E, Cave DR, Wolf DC, Mullin GE, Ketover SR, Legnani PE, Seidman EG (2014) Capsule endoscopy is superior to small-bowel follow-through and equivalent to ileocolonoscopy in suspected Crohn’s disease. Clin Gastroenterol Hepatol 12(4):609–615

    Article  Google Scholar 

  377. Ho IK, Cash BD, Cohen H, Hanauer SB, Inkster M, Johnson DA, Maher MM, Rex DK, Saad A, Singh A (2014) Radiation exposure in gastroenterology: improving patient and staff protection. Am J Gastroenterol 109(8):1180–1194

    Article  Google Scholar 

  378. Allez M, Lemann M, Bonnet J, Cattan P, Jian R, Modigliani R (2002) Long term outcome of patients with active Crohn’s disease exhibiting extensive and deep ulcerations at colonoscopy1. Am J Gastroenterol 97(4):947–953

    Google Scholar 

  379. Baert F, Moortgat L, Van Assche G, Caenepeel P, Vergauwe P, De Vos M, Stokkers P, Hommes D, Rutgeerts P, Vermeire S (2010) Mucosal healing predicts sustained clinical remission in patients with early-stage Crohn’s disease. Gastroenterology 138(2):463–468

    Article  Google Scholar 

  380. Kopylov U, Yablecovitch D, Lahat A, Neuman S, Levhar N, Greener T, Klang E, Rozendorn N, Amitai MM, Ben-Horin S (2015) Detection of small bowel mucosal healing and deep remission in patients with known small bowel Crohn’s disease using biomarkers, capsule endoscopy, and imaging. Am J Gastroenterol 110(9):1316–1323

    Article  Google Scholar 

  381. Phillips CJ (2006) Economic burden of chronic pain. Expert Rev Pharmacoecon Outcomes Res 6(5):591–601

    Article  Google Scholar 

  382. Williams C (2015) Pain drain: the economic and social costs of chronic pain. https://theconversation.com/pain-drain-the-economic-and-social-costs-of-chronic-pain-49666. Accessed Mar 2018

  383. Simon LS (2012) Relieving pain in America: a blueprint for transforming prevention, care, education, and research. J Pain Palliat Care Pharmacother 26(2):197–198

    Article  Google Scholar 

  384. Breivik H, Eisenberg E, O’Brien T (2013) The individual and societal burden of chronic pain in Europe: the case for strategic prioritisation and action to improve knowledge and availability of appropriate care. BMC Public Hhealth 13:1229. https://doi.org/10.1186/1471-2458-13-1229

    Article  Google Scholar 

  385. Prichep LS, John ER, Howard B, Merkin H, Hiesiger EM (2011) Evaluation of the pain matrix using EEG source localization: a feasibility study. Pain Med 12(8):1241–1248

    Article  Google Scholar 

  386. dos Santos Pinheiro ES, de Queirós FC, Montoya P, Santos CL, do Nascimento MA, Ito CH, Silva M, Santos DBN, Benevides S, Miranda JGV (2016) Electroencephalographic patterns in chronic pain: a systematic review of the literature. PLoS One 11(2):e0149085

    Article  Google Scholar 

  387. PainQx (2018) Objective pain measurement. https://www.painqx.com/about-us. Accessed Mar 2018

  388. Waber RL, Shiv B, Carmon Z, Ariely D (2008) Commercial features of placebo and therapeutic. JAMA 299(9):1016–1017

    Article  Google Scholar 

  389. Krauth C, Bartling T (2017) Lohnt sich Rehabilitation? Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz 60(4):394–401

    Article  Google Scholar 

  390. Jitaree S, Phinyomark A, Hu H, Phukpattaranont P, Limsakul C (2012) Design of EMG biofeedback system for lower-limb exercises of the elderly using video games. J Sports Sci Health 13(2):S175–S187

    Google Scholar 

  391. Kim K, Kang J, Lee Y, Moon C, Choi H, Mun C (2011) The development of muscle training system using the electromyogram and interactive game for physical rehabilitation. In: International Conference on Biomedical Engineering, Kuala Lumpur, Malaysia, 20–23 June 2011

    Google Scholar 

  392. Lyons G, Sharma P, Baker M, O’Malley S, Shanahan A (2003) A computer game-based EMG biofeedback system for muscle rehabilitation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 17–21 September 2003

    Google Scholar 

  393. Reyes HC, Arteaga JM (2016) Multidisciplinary production of interactive environments to support occupational therapies. J Biomed Inform 63:90–99

    Article  Google Scholar 

  394. Reyes HC, Arteaga JM (2018) Occupational therapy for people with physical disability using interactive environments. Universal Access Inf Soc 17(1):67–81

    Article  Google Scholar 

  395. Joshi CD, Lahiri U, Thakor NV (2013) Classification of gait phases from lower limb EMG: application to exoskeleton orthosis. In: Point-of-Care Healthcare Technologies, Bangalore, India, 16–18 January 2013

    Google Scholar 

  396. Cavallaro EE, Rosen J, Perry JC, Burns S (2006) Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Trans Biomed Eng 53(11):2387–2396

    Article  Google Scholar 

  397. Kiguchi K, Kariya S, Watanabe K, Izumi K, Fukuda T (2001) An exoskeletal robot for human elbow motion support-sensor fusion, adaptation, and control. IEEE Trans Syst Man Cybern B Cybern 31(3):353–361

    Article  Google Scholar 

  398. Rosen J, Fuchs MB, Arcan M (1999) Performances of Hill-type and neural network muscle models—toward a myosignal-based exoskeleton. Comput Biomed Res 32(5):415–439

    Article  Google Scholar 

  399. Song R, Tong K (2005) Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations. Med Biol Eng Comput 43(4):473–480

    Article  MathSciNet  Google Scholar 

  400. Lenzi T, De Rossi SMM, Vitiello N, Carrozza MC (2012) Intention-based EMG control for powered exoskeletons. IEEE Trans Biomed Eng 59(8):2180–2190

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Pharmacology, University of Cambridge, Cambridge, UK

    Gita Khalili Moghaddam & Christopher R. Lowe

  2. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK

    Gita Khalili Moghaddam & Christopher R. Lowe

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  1. Gita Khalili Moghaddam
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Correspondence to Gita Khalili Moghaddam .

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Khalili Moghaddam, G., Lowe, C.R. (2019). Ex Vivo Biosignatures. In: Health and Wellness Measurement Approaches for Mobile Healthcare. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-01557-2_3

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