Physical Activity

  • Ricard Delgado-GonzaloEmail author
  • Philippe Renevey
  • Alia Lemkaddem
  • Mathieu Lemay
  • Josep Solà
  • Ilkka Korhonen
  • Mattia Bertschi


We begin by briefly introducing the basics of the most frequently used sensors in nowadays wearables targeting a profiling of human physical activity: inertial, biopotential, bioimpedance, and optical sensors. The backbone of the analysis is given to human kinetics and cardiac activity, which are explored in depth in the context of activity profiling in the following sections. Then, an overview of systems for assessing the energy expenditure, calorie consumption, and recovery is presented. Finally, a framework for scientifically evaluating the accuracy of the individual systems is presented.


Wearable Attachable Energy expenditure Fitness Wellness Healthcare Monitoring Activity tracking Heart rate Heart rate variability Photoplethysmography Recovery Sleep EPOC Validation 


  1. 1.
    Fox, S., & Duggan, M. (2013). Tracking for health. Washington, DC: Pew Research Center’s Internet & American Life Project.Google Scholar
  2. 2.
    Müller-Riemenschneider, F., Reinhold, T., Berghöfer, A., & Willich, S. (2008). Health-economic burden of obesity in Europe. European Journal of Epidemiology, 23(8), 499–509.CrossRefGoogle Scholar
  3. 3.
    World Health Organization. (2013). Prevention and Control of Noncommunicable Diseases in the European Region: A Progress Report.Google Scholar
  4. 4.
    Jensen, M., Ryan, D., Apovian, C., Ard, J., Comuzzie, A., Donato, K., Hu, F. H. V., Jakicic, J., Kushner, R., Loria, C., Millen, B., Nonas, C., Pi-Sunyer, F. S. J., Stevens, V., Wadden, T., & Wolfe, B. Y. S. (2014). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults. Circulation, 129(25 suppl 2), S102–S138.CrossRefGoogle Scholar
  5. 5.
    Brajdic, A., & Harle, R. (2013). Walk detection and step counting on unconstrained smartphones. In Proceedings of the 2013 ACM international joint conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Zurich.Google Scholar
  6. 6.
    VanWormer, J. (2004). Pedometers and brief E-counseling: Increasing physical activity for overweight adults. Journal of Applied Behavavior Analysis, 37(3), 421–425.CrossRefGoogle Scholar
  7. 7.
    Le Masurier, G., Sidman, C., & Corbin, C. (2003). Accumulating 10,000 steps: Does this meet current physical activity guidelines? Research Quarterly for Exercise and Sport, 74(4), 389–394.CrossRefGoogle Scholar
  8. 8.
    Yamamura, C., Tanaka, S., Futami, J., Oka, J., Ishikawa-Takata, K., & Kashiwazaki, H. (2003). Activity diary method for predicting energy expenditure as evaluated by a whole-body indirect human calorimeter. Journal of Nutritional Science and Vitaminology, 49(4), 262–269.CrossRefGoogle Scholar
  9. 9.
    Stroud, M., Coward, W., & Sawyer, M. (1993). Measurements of energy expenditure using isotope-labelled water (2H2 180) during an arctic expedition. European Journal of Applied Physiology and Occupational Physiology, 67(4), 375–379.CrossRefGoogle Scholar
  10. 10.
    Levine, J. (2005). Measurement of energy expenditure. Public Health Nutrition, 8(7A), 1123–1132.CrossRefGoogle Scholar
  11. 11.
    Delgado-Gonzalo, R., Renevey, P., Calvo, E., Solà, J., Lanting, C., Bertschi, M., & Lemay, M. (2014) Human energy expenditure models: Beyond state-of-the-art commercialized embedded algorithms. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, Vol. 8529, pp. 3–14.Google Scholar
  12. 12.
    Bonomi, A., Plasqui, G., Goris, A., & Westerterp, K. (2009). Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. Journal of Applied Physiology, 107(3), 655–661.CrossRefGoogle Scholar
  13. 13.
    Rumo, M., Amft, O., Tröster, G., & Mäder, U. (2011). A stepwise validation of a wearable system for estimating energy expenditure in field-based research. Physiological Measurement, 32(12), 1983–2001.CrossRefGoogle Scholar
  14. 14.
    van Hees, V., & Ekelund, U. (2009). Novel daily energy expenditure estimation by using objective activity type classification: Where do we go from here? Journal of Applied Physiology, 107(3), 639–640.CrossRefGoogle Scholar
  15. 15.
    Charlot, K., Cornolo, J., Borne, R., Brugniaux, J., Richalet, J.-P., Chapelot, D., & Pichon, A. (2014). Improvement of energy expenditure prediction from heart rate during running. Physiological Measurement, 35(2), 253–266.CrossRefGoogle Scholar
  16. 16.
    Halson, S. (2014). Monitoring training load to understand fatigue in athletes. Sports Medicine, 44(2), 139–147.CrossRefGoogle Scholar
  17. 17.
    Vales-Alonso, J. (2010). Ambient intelligence systems for personalized sport training. Sensors, 10(3), 2359–2385.CrossRefGoogle Scholar
  18. 18.
    Buchheit, M. (2014). Monitoring training status with HR measures: Do all roads lead to Rome? Frontiers in Physiology, 5, 1–19.CrossRefGoogle Scholar
  19. 19.
    Zhang, Z., Zhouyue, P., & Benyuan, L. (2015). TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 62(2), 522–531.CrossRefGoogle Scholar
  20. 20.
    Hongu, N., Orr, B., Roe, D., Reed, R., & Going, S. (2013). Global positioning system watches for estimating energy expenditure. The Journal of Strength & Conditioning Research, 27(11), 3216–3220.CrossRefGoogle Scholar
  21. 21.
    Van Hoye, K., Mortelmans, P., & Lefevre, J. (2014). Validation of the sensewear pro3 armband using an incremental exercise test. The Journal of Strength & Conditioning Research, 28(10), 2806–2814.CrossRefGoogle Scholar
  22. 22.
    Santos, D., Silva, A., Matias, C., Magalhães, J., Fields, D., Minderico, C., Ekelund, U., & Sardinha, L. (2014). Validity of a combined heart rate and motion sensor for the measurement of free-living energy expenditure in very active individuals. Journal of Science and Medicine in Sport, 17(4), 387–393.CrossRefGoogle Scholar
  23. 23.
    Pavel, M., Jimison, H., Korhonen, I., Gordon, C., & Saranummi, N. (2015). Behavioral informatics and computational modeling in support of proactive health management and care. IEEE Transactions on Biomedical Engineering, 62(12), 2763–2775.CrossRefGoogle Scholar
  24. 24.
    Rivera-Ruiz, M., Cajavilca, C., & Varon, J. (2008). Einthoven’s string galvanometer – The first electrocardiograph. Texas Heart Institute Journal, 35(2), 174–178.Google Scholar
  25. 25.
    Bayford, R., & Tizzard, A. (2012). Bioimpedance imaging: An overview of potential clinical applications. Analyst, 137(20), 4635–4643.CrossRefGoogle Scholar
  26. 26.
    Thomasset, A. (1962). Bio-electrical properties of tissue impedance measurements. Lyon Medical, 207, 107–118.Google Scholar
  27. 27.
    Atzler, E., & Lehmann, G. (1932). Über ein neues Verfahren zur Darstellung der Herztätigkeit (Dielektrographie). Arbeitsphysiologie, 5(6), 636–680.Google Scholar
  28. 28.
    Gedde, L., & Hoff, H. (1964). The measurement of physiologic events by electrical impedance. American Journal of Medical Electronics, 3(1), 16–27.Google Scholar
  29. 29.
    Tishchenko, M., Smirnov, A., Danilov, L., & Aleksandrov, A. (1973). Characteristics and clinical use of integral rheography – A new method of measuring the stroke volume. Kardiologiia, 13(11), 54–62.Google Scholar
  30. 30.
    Henderson, R., & Webster, J. (1978). An Impedance camera for spatially specific measurements of the thorax. IEEE Transactions on Biomedical Engineering, 25(3), 250–254.CrossRefGoogle Scholar
  31. 31.
    Barbe, D., & Brown, B. (1984). Applied potential tomography. Journal of Physics E: Scientific Instruments, 17(9), 723–733.CrossRefGoogle Scholar
  32. 32.
    Holder, D. (2005). Electrical impedance tomography: Methods, history and applications. London: Institute of Physics Publishing.Google Scholar
  33. 33.
    Renevey, P., Solà, J., Theurillat, P., Bertschi, M., Krauss, J., Andries, D., & Sartori, C. (2013, July 3–7). Validation of a wrist monitor for accurate estimation of RR intervals during sleep. In Proceedings of the 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), Osaka.Google Scholar
  34. 34.
    Shelley, K., Tamai, D., Jablonka, D., Gesquiere, M., Stout, R., & Silverman, D. (2005). The effect of venous pulsation on the forehead pulse oximeter wave form as a possible source of error in Spo2 calculation. Anesthesia & Analgesia, 100(3), 743–747.CrossRefGoogle Scholar
  35. 35.
    Delgado-Gonzalo, R., Celka, R., Renevey, P., Dasen, S., Solà, J., Bertschi, M., & Lemay, M. (2015, August 25–29). Physical activity profiling: Activity-specific step counting and energy expenditure models using 3D wrist acceleration. In Proceedings of the 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC'15), Milano.Google Scholar
  36. 36.
    Bouten, C., Sauren, A., Verduin, M., & Janssen, J. (1997). Effects of placement and orientation of body-fixed accelerometers on the assessment of energy expenditure during walking. Medical and Biological Engineering and Computing, 35(1), 50–56.CrossRefGoogle Scholar
  37. 37.
    Mathie, M., Coster, A., Lovell, N., & Celler, B. (2004). Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25(2), R1–R20.CrossRefGoogle Scholar
  38. 38.
    Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., & Korhonen, I. (2006). Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 10(1), 119–128.CrossRefGoogle Scholar
  39. 39.
    Mathie, M., Foster, A., Lovell, N., & Celler, B. (2003). Detection of daily physical activities using a triaxial accelerometers. Medical and Biological Engineering and Computing, 41(3), 296–301.CrossRefGoogle Scholar
  40. 40.
    Aminian, K., Robert, P., Buchser, E., Rutschmann, B., Hayoz, D., & Depairon, M. (1999). Physical activity monitoring based on accelerometry: Validation and comparison with video observation. Medical & Biological Engineering & Computing, 37(3), 304–308.CrossRefGoogle Scholar
  41. 41.
    Foerster, F., & Fahrenberg, J. (2000). Motion pattern and posture: Correctly assessed by calibrated accelerometers. Behavior Research Methods, Instruments, & Computers, 32(3), 450–457.CrossRefGoogle Scholar
  42. 42.
    Ng, J., Sahakian, A., & Swiryn, S. (2003). Accelerometer-based body-position sensing for ambulatory electrocardiographic monitoring. Biomedical Instrumentation & Technology, 37(5), 338–346.Google Scholar
  43. 43.
    Foerster, F., Smeja, M., & Fahrenberg, J. (1999). Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring. Computers in Human Behavior, 15(5), 571–583.CrossRefGoogle Scholar
  44. 44.
    Ermes, M., Pärkkä, J., & Mäntyjärvi, I. K. J. (2008). Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine, 12(1), 20–26.CrossRefGoogle Scholar
  45. 45.
    Delgado-Gonzalo, R., Lemkaddem, A., Renevey, P., Calvo, E., Lemay, M., Cox, K., Ashby, D., Willardson, J., & Bertschi, M. (2016, August 16-20). Real-time monitoring of swimming performance. In Proceedings of the 38th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16), Orlando.Google Scholar
  46. 46.
    Lee, T. H. (2004 July). Calories Burned in 30 Minutes for People of Three Different Weights. Harvard Heart Letter (14).Google Scholar
  47. 47.
    Cucchiara, R., Grana, C., Prati, A., & Vezzani, R. (2005). Probabilistic posture classification for human-behavior analysis. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 35(1), 42–54.CrossRefGoogle Scholar
  48. 48.
    Cohen, I., & Li, H. (2003). Inference of human postures by classification of 3D human body shape. In Proceedings of the IEEE international workshop on Analysis and Modeling of Faces and Gestures, (AMFG 2003).Google Scholar
  49. 49.
    Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., & Fuks, H. (2012, October 20–25). Wearable computing: Accelerometers’ data classification of body postures and movements. In Proceedings of the 21th Brazilian Symposium on Artificial Intelligence – Advances in Artificial Intelligence (SBIA 2012), Curitiba, Brazil.Google Scholar
  50. 50.
    Juang, C., & Chang, C. (2007). Human body posture classification by a neural fuzzy network and home care system application. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 37(6), 984–994.CrossRefGoogle Scholar
  51. 51.
    Chételat, O., Ferrario, D., Proença, M., Porchet, J.-A., Falhi, A., Grossenbacher, O., Delgado-Gonzalo, R., Della Ricca, N., & Sartori, C. (2015, August 25–29). Clinical validation of LTMS-S: A wearable system for vital signs monitoring. In Proceedings of the 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan.Google Scholar
  52. 52.
    Mota, S., & Picard, R. (2003, June 16–22). Automated posture analysis for detecting learner’s interest level. In Proceedings of the conference on Computer Vision and Pattern Recognition Workshop (CVPRW'03), Madison.Google Scholar
  53. 53.
    Evenson, K., Goto, M., & Furberg, R. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(159), 1–22.Google Scholar
  54. 54.
    Schubert, A., Kempf, J., & Heiderscheit, B. (May 2014). Influence of stride frequency and length on running mechanics: A systematic review. Sports Health: A Multidisciplinary Approach, 6(3), 210–217.CrossRefGoogle Scholar
  55. 55.
    Townshend, A., Worringham, C., & Stewart, I. (2008). Assessment of speed and position during human locomotion using nondifferential GPS. Medicine and Science in Sports and Exercise, 40(1), 124.CrossRefGoogle Scholar
  56. 56.
    Akenhead, R., French, D., Thompson, K., & Hayes, P. (2014). The acceleration dependent validity and reliability of 10Hz GPS. Journal of Science and Medicine in Sport, 17(5), 562–566.CrossRefGoogle Scholar
  57. 57.
    Rampinini, E., Alberti, G., Fiorenza, M., Riggio, M., Sassi, R., Borges, T., & Coutts, A. (2015). Accuracy of GPS devices for measuring high-intensity running in field-based team sports. International Journal of Sports Medicine, 36(1), 49–53.Google Scholar
  58. 58.
    Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Medicine, 43(10), 1025–1042.CrossRefGoogle Scholar
  59. 59.
    Varley, M., Fairweather, I., & Aughey, R. (2012). Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. Journal of Sports Sciences, 30(2), 121–127.CrossRefGoogle Scholar
  60. 60.
    Dwyer, D., & Gabbett, T. (2012). Global positioning system data analysis: Velocity ranges and a new definition of sprinting for field sport athletes. The Journal of Strength & Conditioning Research, 26(3), 818–824.CrossRefGoogle Scholar
  61. 61.
    Yang, S., Mohr, C., & Li, Q. (2011). Ambulatory running speed estimation using an inertial sensor. Gait & Posture, 34(4), 462–466.CrossRefGoogle Scholar
  62. 62.
    Yang, S., & Li, Q. (2012). Inertial sensor-based methods in walking speed estimation: A systematic review. Sensors, 15(5), 6102–6116.CrossRefGoogle Scholar
  63. 63.
    Sabatini, A., Martelloni, C., Scapellato, S., & Cavallo, F. (2005). Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering, 52(3), 486–494.CrossRefGoogle Scholar
  64. 64.
    Mannini, A., & Sabatini, A. (2011). On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines. In Proceedings of the 2011 33rd annual international conference of the IEEE Engineering in Medicine and Biology Society, Boston.Google Scholar
  65. 65.
    Lugade, V., Fortune, E., Morrow, M., & Kaufman, K. (2014). Validity of using tri-axial accelerometers to measure human movement – Part I: Posture and movement detection. Medical Engineering & Physics, 36(2), 169–176.CrossRefGoogle Scholar
  66. 66.
    Lugade, V., Fortune, E., Morrow, M., & Kaufman, K. (2014). Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities. Medical Engineering & Physics, 36(6), 659–669.CrossRefGoogle Scholar
  67. 67.
    Fortune, E., Lugade, V., & Kaufman, K. (2014). Posture and movement classification: The comparison of tri-axial accelerometer numbers and anatomical placement. Journal of Biomechanical Engineering, 136(5), 051003.CrossRefGoogle Scholar
  68. 68.
    Skotte, J., Korshøj, M., Kristiansen, J., Hanisch, C., & Holtermann, A. (2014). Detection of physical activity types using triaxial accelerometers. Journal of Physical Activity & Health, 11(1), 76–84.CrossRefGoogle Scholar
  69. 69.
    Bertschi, M., Celka, P., Delgado-Gonzalo, R., Lemay, M., Calvo, E., Grossenbacher, O., & Renevey, P. (2015, August 25–29). Accurate walking and running speed estimation using wrist inertial data. In Proceedings of the 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Milano.Google Scholar
  70. 70.
    Watanabe, K., & Hokari, M. (2006). Kinematical analysis and measurement of sports form. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 36(3), 549–557.CrossRefGoogle Scholar
  71. 71.
    Pope, M., Bevins, T., Wilder, D., & Frymoyer, J. (1985). The relationship between anthropometric, postural, muscular, and mobility characteristics of males ages 18–55. Spine, 10(7), 644–648.CrossRefGoogle Scholar
  72. 72.
    Steultjens, M., Dekker, J., van Baar, M., Oostendorp, R., & Bijlsma, J. (2000). Range of joint motion and disability in patients with osteoarthritis of the knee or hip. Rheumatology, 39(9), 955–961.CrossRefGoogle Scholar
  73. 73.
    Moore, S., MacDougall, H., & Ondo, W. (2008). Ambulatory monitoring of freezing of gait in Parkinson’s disease. Journal of Neuroscience Methods, 167(2), 340–348.CrossRefGoogle Scholar
  74. 74.
    Kim, C., & Eng, J. (2004). Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: Relationship to walking speed. Gait & Posture, 20(2), 140–146.CrossRefGoogle Scholar
  75. 75.
    Casadio, M., Morasso, P., & Sanguineti, V. (2005). Direct measurement of ankle stiffness during quiet standing: Implications for control modelling and clinical application. Gait & Posture, 21(4), 410–424.CrossRefGoogle Scholar
  76. 76.
    Tao, W., Liu, T., Zheng, R., & Feng, H. (2012). Gait analysis using wearable sensors. Sensors, 12(2), 2255–2283.CrossRefGoogle Scholar
  77. 77.
    Bamberg, S., Benbasat, A., Scarborough, D., Krebs, D., & Paradiso, J. (2008). Gait analysis using a shoe-integrated wireless sensor systems. IEEE Transactions on Information Technology in Biomedicine, 12(4), 413–423.CrossRefGoogle Scholar
  78. 78.
    Liu, T., Inoue, Y., & Shibata, K. (2010). A wearable ground reaction force sensor system and its application to the measurement of extrinsic gait variability. Sensors, 10(11), 10240–10255.CrossRefGoogle Scholar
  79. 79.
    Delgado-Gonzalo, R., Hubbard, J., Renevey, P., Lemkaddem, A., Vellinga, Q., Ashby, D., Willardson, J., & Bertschi, M. (2017, July 11–15). Real-time gait analysis with accelerometer-based smart shoes. In Proceedings of the 39th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju.Google Scholar
  80. 80.
    Ng, S., & Chizeck, H. (2002). Fuzzy model identification for classification of gait events in paraplegics. IEEE Transactions on Fuzzy Systems, 5(4), 536–544.Google Scholar
  81. 81.
    Savelberg, H., & de Lange, A. (1999). Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks. Clinical Biomechanics, 14(8), 585–592.CrossRefGoogle Scholar
  82. 82.
    Forner-Cordero, A., Koopman, H., & van der Helm, F. (2004). Use of pressure insoles to calculate the complete ground reaction forces. Journal of Biomechanics, 37(9), 1427–1432.CrossRefGoogle Scholar
  83. 83.
    Davey, N., Anderson, M., & James, D. (2008). Validation trial of an accelerometer-based sensor platform for swimming. Sports Technology, 1(4-5), 202–207.CrossRefGoogle Scholar
  84. 84.
    Le Sage, T., Bindel, A., Conway, P., Justham, L., Slawson, S., & West, A. (2011). Embedded programming and real time signal processing of swimming strokes. Sports Engineering, 14(1), 1–14.CrossRefGoogle Scholar
  85. 85.
    Bächlin, M., & Tröster, G. (2012). Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive and Mobile Computing, 8(1), 68–81.CrossRefGoogle Scholar
  86. 86.
    Chakravorti, N., Le Sage, T., Slawson, S., Conway, P., & West, A. (2013). Design & implementation of an integrated performance monitoring tool for swimming to extract stroke information at real time. IEEE Transactions on Human-Machine Systems, 43(2), 199–213.CrossRefGoogle Scholar
  87. 87.
    Hagem, R., O’Keefe, S., Fickenscher, T., & Thiel, D. (2013). Self contained adaptable optical wireless communications system for stroke rate during swimming. IEEE Sensors Journal, 13(8), 3144–3151.CrossRefGoogle Scholar
  88. 88.
    Beanland, E., Main, L., Aisbett, B., Gastin, P., & Netto, K. (2014). Validation of GPS and accelerometer technology in swimming. Journal of Science and Medicine in Sport, 17(2), 234–238.CrossRefGoogle Scholar
  89. 89.
    Mooney, R., Corley, G., Godfrey, A., Quinlan, L., & ÓLaighin, G. (2016). Inertial sensor technology for elite swimming performance analysis: A systematic review. Sensors, 16(1), 18.CrossRefGoogle Scholar
  90. 90.
    Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology (1996 March). Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation, 93, 1043–1065.Google Scholar
  91. 91.
    Guzzetti, S., Dassi, S., Pecis, M., Casati, R., Masu, A., Longoni, P., Tinelli, M., Cerutti, S., Pagani, M., & Malliani, A. (1991). Altered pattern of circadian neural control of heart period in mild hypertension. Journal of Hypertension, 9(9), 831–838.CrossRefGoogle Scholar
  92. 92.
    Huikuri, H., Valkama, J., Seppänen, T., Kessler, K., Takkunen, J., & Myerburg, R. (1993). Frequency domain measures of heart rate variability before the onset of nonsustained and sustained ventricular tachycardia in patients with coronary artery disease. Circulation, 87(4), 1220–1228.CrossRefGoogle Scholar
  93. 93.
    Al-Rawahi, N., & Green, M. (2007). Diagnosis of supraventricular tachycardia. The Journal of the Association of Physicians of India, 55, 21–24.Google Scholar
  94. 94.
    Dougherty, C. M., & Burr, R. L. (1992). Comparison of heart rate variability in survivors and nonsurvivors of sudden cardiac arrest. The American Journal of Cardiology, 70(4), 441–448.CrossRefGoogle Scholar
  95. 95.
    Algra, A., Tijssen, J., Roelandt, J., Pool, J., & Lubsen, J. (1993). Heart rate variability from 24-hour electrocardiography and the 2-year risk for sudden death. Circulation, 88(1), 180–185.CrossRefGoogle Scholar
  96. 96.
    Cinaz, B., Arnrich, B., Marca, R., & Tröster, G. (2013). Monitoring of mental workload levels during an everyday life office-work scenario. Personal and Ubiquitous Computing, 17(2), 229–239.CrossRefGoogle Scholar
  97. 97.
    Luque-Casado, A., Perales, J., Cárdenas, D., & Sanabria, D. (2016). Heart rate variability and cognitive processing: The autonomic response to task demands. Biological Psychology, 113, 83–90.CrossRefGoogle Scholar
  98. 98.
    Pendleton, D., Sakalik, M., Moore, M., & Phillip, T. (2016). Mental engagement during cognitive and psychomotor tasks: Effects of task type, processing demands, and practice. International Journal of Psychophysiology, 109, 124–131.CrossRefGoogle Scholar
  99. 99.
    Taelman, J., Vandeput, S., Spaepen, A., & Van Huffel, S. (2008). Influence of mental stress on heart rate and heart rate variability. IFMBE proceedings (pp. 1366–1369).Google Scholar
  100. 100.
    Khushaba, R., Kodagoda, S., Lal, S., & Dissanayake, G. (2011). Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Transactions on Biomedical Engineering, 58(1), 121–131.CrossRefGoogle Scholar
  101. 101.
    Patel, M., Lal, S., Kavanagh, D., & Rossiter, P. (2011). Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Systems with Applications, 38(6), 7235–7242.CrossRefGoogle Scholar
  102. 102.
    Crouter, S., Churilla, J., & Bassett, D. (2006). Estimating energy expenditure using accelerometers. European Journal of Applied Physiology, 98(6), 601–612.CrossRefGoogle Scholar
  103. 103.
    Crouter, S., Kuffel, E., Haas, J., Frongillo, E., & Bassett, D. (2010). A refined 2-regression model for the ActiGraph accelerometer. Medicine and Science in Sports and Exercise, 42(5), 1029–1037.CrossRefGoogle Scholar
  104. 104.
    Brage, S., Brage, N., Franks, P., Ekelund, U., Wong, M., Andersen, L., Froberg, K., & Wareham, N. (2004). Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. Journal of Applied Physiology, 96(1), 343–351.CrossRefGoogle Scholar
  105. 105.
    Altini, M., Penders, J., & Amft, O. (2012, October 23–25). Energy expenditure estimation using wearable sensors: A new methodology for activity-specific models. In Proceedings of the conference on Wireless Health (WH’12), San Diego.Google Scholar
  106. 106.
    Albinali, F., Intille, S., Haskell, W., & Rosenberger, M. (2010, September 26–29). Using wearable activity type detection to improve physical activity energy expenditure estimation. In Proceedings of the 12th ACM international conference on Ubiquitous computing (Ubicomp’10), New York.Google Scholar
  107. 107.
    Koutedakis, Y., Metsios, G., & Stavropoulos-Kalinoglou, A. (2006). Periodization of exercise training in sport. In The physiology of training. Elsevier, London, UK.Google Scholar
  108. 108.
    Halson, S., & Jeukendrup, A. (2004). Does overtraining exist? An analysis of overreaching and overtraining research. Sports Medicine, 34(14), 967–981.CrossRefGoogle Scholar
  109. 109.
    Bielinski, R., Schutz, Y., & Jéquier, E. (1985). Energy metabolism during the postexercise recovery in man. The American Journal of Clinical Nutrition, 42(1), 69–82.Google Scholar
  110. 110.
    Laforgia, J., Withers, R., & Gore, C. (2006). Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. Journal of Sports Sciences, 24(12), 1247–1264.CrossRefGoogle Scholar
  111. 111.
    Børsheim, E., & Bahr, R. (2003). Effect of exercise intensity, duration and mode on post-exercise oxygen consumption. Sports Medicine, 33(14), 1037–1060.CrossRefGoogle Scholar
  112. 112.
    Sedlock, D., Fissinger, J., & Melby, C. (1989). Effect of exercise intensity and duration on postexercise energy expenditure. Medicine & Science in Sports & Exercise, 21(6), 662–666.Google Scholar
  113. 113.
    Sedlock, D. (1991). Effect of exercise intensity on postexercise energy expenditure in women. British Journal of Sports Medicine, 25(1), 38–40.CrossRefGoogle Scholar
  114. 114.
    Rusko, H., Pulkkinen, A., Saalasti, S., Hynynen, E., & Kettunen, J. (2003, May 28–31). Pre-prediction of EPOC: A tool for monitoring fatigue accumulation during exercise? In 50th Annual Meeting of the American College of Sports Medicine, San Francisco.Google Scholar
  115. 115.
    Manzoni, C., Carrard, A., Fontana, E., Lemay, M., Bertschi, M., & Delgado-Gonzalo, R. (2017, July 11–15). Towards VO2 monitoring – Validation of a heart rate based algorithm. In Proceedings of the 39th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju.Google Scholar
  116. 116.
    Reilly, T., & Edwards, B. (2007). Altered sleep–wake cycles and physical performance in athletes. Physiology & Behavior, 90(2-3), 274–284.CrossRefGoogle Scholar
  117. 117.
    Hausswirth, C., Louis, J., Aubry, A., Bonnet, G., Duffield, R., & Meur, Y. L. (2014). Evidence of disturbed sleep and increased illness in overreached endurance athletes. Medicine & Science in Sports & Exercise, 46(5), 1036–1045.CrossRefGoogle Scholar
  118. 118.
    Hall, M., Vasko, R., Buysse, D., Ombao, H., Chen, Q., Cashmere, J. D., Kupfer, D., & Thayer, J. (2004). Acute stress affects heart rate variability during sleep. Psychosomatic Medicine, 66(1), 56–62.CrossRefGoogle Scholar
  119. 119.
    Brown, R., Basheer, R., McKenna, J., Strecker, R., & McCarley, R. (2012). Control of sleep and wakefulness. Physiological Reviews, 92(3), 1087–1187.CrossRefGoogle Scholar
  120. 120.
    Buguet, A., Roussel, B., Angus, R., Sabiston, B., & Radomski, M. (1980). Human sleep and adrenal individual reactions to exercise. Electroencephalography and Clinical Neurophysiology, 49(5-6), 515–523.CrossRefGoogle Scholar
  121. 121.
    Green, S. (2011). Biological rhythms, sleep and hypnosis (p. 200). London: Palgrave Macmillan.CrossRefGoogle Scholar
  122. 122.
    Renevey, P., Delgado-Gonzalo, R., Lemkaddem, A., Proença, M., Lemay, M., Solà, J., Tarniceriu, A., & Bertschi, M. (2017, June 11–15). Optical wrist-worn device for sleep monitoring. In Proceedings of the joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), Tampere.Google Scholar
  123. 123.
    Middlekoop, H., Hilten, B. V., Kramer, C., & Kamphuisen, H. (1993). Actigraphically recorded motor activity and immobility across sleep cycles and stages in healthy male subjects. Journal of Sleep Research, 2(1), 28–33.CrossRefGoogle Scholar
  124. 124.
    Mendez, M., Matteucci, M., Cerutti, S., Aletti, F., & Bianchi, A. (2009, September 2–6). Sleep staging classification based on HRV: Time-variant analysis. In Proceedings of the 2009 annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS'09), Minneapolis.Google Scholar
  125. 125.
    Roebuck, A., Monasterio, V., Gederi, E., Osipov, M., Behar, J., Malhotra, A., Penzel, T., & Clifford, G. (2014). A review of signals used in sleep analysis. Physiological Measurement, 35(1), R1–R57.CrossRefGoogle Scholar
  126. 126.
    International Organization for Standardization. (2011). Clinical investigation of medical devices for human subjects–Good clinical practice (ISO Standard No. 14155). Retrieved from
  127. 127.
    Lee, J., Kim, Y., & Welk, G. (2014). Validity of consumer-based physical activity monitors. Medicine & Science in Sports & Exercise, 46(9), 1840–1848.CrossRefGoogle Scholar
  128. 128.
    Ryan, J., Walsh, M., & Gormley, J. (2014). A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy. Journal of Neuroengineering and Rehabilitation, 11(1), 116.CrossRefGoogle Scholar
  129. 129.
    Dannecker, K., Sazonova, N., Melanson, E., Sazonov, E., & Browning, R. (2013). A comparison of energy expenditure estimation of several physical activity monitors. Medicine and Science in Sports and Exercise, 45(11), 2105–2112.CrossRefGoogle Scholar
  130. 130.
    Riffenburgh, R. (2012). Statistics in medicine. Academic Press, London, UK.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ricard Delgado-Gonzalo
    • 1
    Email author
  • Philippe Renevey
    • 1
  • Alia Lemkaddem
    • 1
  • Mathieu Lemay
    • 1
  • Josep Solà
    • 1
  • Ilkka Korhonen
    • 2
  • Mattia Bertschi
    • 1
  1. 1.Centre Suisse d’Electronique et de Microtechnique SANeuchâtelSwitzerland
  2. 2.Tampere University of TechnologyTampereFinland

Personalised recommendations