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A smart inertial system for fall detection

  • Bruno Andó
  • Salvatore Baglio
  • Ruben CrispinoEmail author
  • Vincenzo Marletta
Original Research
  • 18 Downloads

Abstract

The high incidence of falls in elderly and people with neurological disease, represents a serious issue which can have tremendous consequences. To reduce the effect on the society of this phenomenon, scientist in every part of the world, have tried to develop solutions ranging from gait training to more technological approaches such as the assistive device. Although it is evident that the scientific community has developed different and valuable paradigms for the ADLs and fall detection issue, in selecting one of the many possible solution, one has to consider the different advantages and disadvantages in terms of performance, user acceptance and power requirements. In order to meet the necessity to develop solutions and paradigms that can be suitable for battery-powered operations, with specific focus to low power embedded systems, in this paper a Fall and ADLs classification paradigm exploiting an event correlated approach is proposed. It is based on the maximum value of the correlation between two signals implemented in an ad-hoc developed embedded system based on an STmicroelectronics microcontroller equipped with sensors and communication facilities. Results confirm the suitability of the presented methodology showing an average sensitivity (\(S_e\)) of 0.97 and an average specificity (\(S_p\)) of 0.97.

Keywords

ADL Classification Embedded architecture Fall detection 

Notes

Acknowledgements

This work has been supported by the SUMMIT grant, funded under Horizon 2020-PON 2014/2020 programme, N. F/050270/03/X32-CUP B68I17000370008-COR: 130150.

References

  1. 3rd report from the Patient Safety Observatory (2017) Slips, trips and falls in hospital. 3rd report from the Patient Safety ObservatoryGoogle Scholar
  2. Agency for Healthcare Research and Quality (2017) The falls management program: a quality improvement initiative for nursing facilities. Agency for Healthcare Research and Quality, RockvilleGoogle Scholar
  3. Agency for Healthcare Research and Quality (2019) Module 3: falls prevention and management. Agency for Healthcare Research and Quality, RockvilleGoogle Scholar
  4. Ando B (2006) Instrumentation notes—sensors that provide security for people with depressed receptors. IEEE Instrum Meas Mag 9(2):56–61.  https://doi.org/10.1109/MIM.2006.1634992 MathSciNetCrossRefGoogle Scholar
  5. Ando B (2008) A smart multisensor approach to assist blind people in specific urban navigation tasks. IEEE Trans Neural Syst Rehab Eng 16(6):592–594.  https://doi.org/10.1109/TNSRE.2008.2003374 CrossRefGoogle Scholar
  6. Ando B, Graziani S (2009) Multisensor strategies to assist blind people: a clear-path indicator. IEEE Trans Instrum Meas 58(8):2488–2494.  https://doi.org/10.1109/TIM.2009.2014616 CrossRefGoogle Scholar
  7. Ando B, Baglio S, Malfa SL, Marletta V (2011) A sensing architecture for mutual user-environment awareness case of study: a mobility aid for the visually impaired. IEEE Sens J 11(3):634–640.  https://doi.org/10.1109/JSEN.2010.2053843 CrossRefGoogle Scholar
  8. Andó B, Baglio S, Lombardo CO, Marletta V, Pergolizzi EA, Pistorio A (2014) An event polarized paradigm for ADL detection in AAL context. In: 2014 IEEE international instrumentation and measurement technology conference (I2MTC) proceedings, pp 1079–1082.  https://doi.org/10.1109/I2MTC.2014.6860908
  9. Andó B, Baglio S, Lombardo C, Marletta V, Pergolizzi E (2015a) Fall and ADL detection methodologies for AAL. Lecture Notes in Electrical Engineering, pp 427–431.  https://doi.org/10.1007/978-3-319-09617-9_75 Google Scholar
  10. Andó B, Baglio S, Lombardo C, Marletta V, Pergolizzi E, Pistorio A, Valastro A (2015b) ADL detection for the active ageing of elderly people. Biosyst Biorobot 11:287–294.  https://doi.org/10.1007/978-3-319-18374-9_27 CrossRefGoogle Scholar
  11. Andò B, Baglio S, Lombardo CO, Marletta V (2015c) A multi-user assistive system for the user safety monitoring in care facilities. In: Proceedings of 2015 IEEE Int Work Meas Networking, M N 2015, pp 112–116.  https://doi.org/10.1109/IWMN.2015.7322983
  12. Andó B, Baglio S, Lombardo CO, Marletta V (2015d) An event polarized paradigm for ADL detection in AAL context. IEEE Trans Instrum Meas 64(7):1814–1825.  https://doi.org/10.1109/TIM.2014.2385144 CrossRefGoogle Scholar
  13. Andó B, Baglio S, Lombardo C, Marletta V (2016a) Smart multi-sensor solutions for ADL detection. Human monitoring, smart health and assisted living: techniques and technologies. IET-The Institution of Engineering TechnologyGoogle Scholar
  14. Andó B, Baglio S, Lombardo CO, Marletta V (2016b) A multisensor data-fusion approach for ADL and fall classification. IEEE Trans Instrum Meas 65(9):1960–1967.  https://doi.org/10.1109/TIM.2016.2552678 CrossRefGoogle Scholar
  15. Andó B, Baglio S, Marletta V, Crispino R (2017) A neurofuzzy approach for fall detection. In: 2017 International conference on engineering, technology and innovation (ICE/ITMC), pp 1312–1316.  https://doi.org/10.1109/ICE.2017.8280032
  16. Andó B, Baglio S, Marletta V, Crispino R, Pistorio A (2018) A measurement strategy to assess the optimal design of an RFID-based navigation aid. In: IEEE transactions on instrumentation and measurement, pp 1–7.  https://doi.org/10.1109/TIM.2018.2879069 CrossRefGoogle Scholar
  17. Ariani A, Redmond SJ, Chang D, Lovell NH (2010) Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors. In: 2010 Annual international conference of the IEEE engineering in medicine and biology, pp 2115–2118.  https://doi.org/10.1109/IEMBS.2010.5627202
  18. Burt CW, Fingerhut LA (1998) Injury visits to hospital emergency departments: United States, 1992–95. Vital Health Stat 13(131):1–76Google Scholar
  19. Cameron ID, Gillespie LD, Robertson MC, Murray GR, Hill KD, Cumming RG, Kerse N (2012) Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst Rev 12(CD005):465.  https://doi.org/10.1002/14651858.CD005465.pub3 CrossRefGoogle Scholar
  20. CDC-Centers for Disease Control and Prevention (2016) Hip fractures among older adults—home and recreational safety. CDC Injury CenterGoogle Scholar
  21. Chaudhuri S, Thompson H, Demiris G (2014) Fall detection devices and their use with older adults. J Geriatr Phys Ther 37(4):178–196.  https://doi.org/10.1519/JPT.0b013e3182abe779 CrossRefGoogle Scholar
  22. Cippitelli E, Gasparrini S, Gambi E, Spinsante S, Wåhslény J, Orhany I, Lindhy T (2015) Time synchronization and data fusion for RGB-Depth cameras and inertial sensors in AAL applications. In: 2015 IEEE Int Conf Commun Work ICCW 2015, pp 265–270.  https://doi.org/10.1109/ICCW.2015.7247189
  23. Dunkel J, Bruns R, Stipkovic S (2013) Event-based smartphone sensor processing for ambient assisted living. In: 2013 IEEE Elev. Int. Symp. Auton. Decentralized Syst., IEEE, pp 1–6.  https://doi.org/10.1109/ISADS.2013.6513422
  24. Fabbri E, Zoli M, Gonzalez-Freire M, Salive ME, Studenski SA, Ferrucci L (2015) Aging and multimorbidity: new tasks, priorities, and frontiers for integrated gerontological and clinical research.  https://doi.org/10.1016/j.jamda.2015.03.013 CrossRefGoogle Scholar
  25. Fahim M, Fatima I, Lee S, Lee Y (2012) Daily life activity tracking application for smart homes using android smartphone. In: 2012 14th International conference on advanced communication technology (ICACT), pp 241–245Google Scholar
  26. Ghazaleh P, Nasser M, Arne L, Peter H (2012) Chest-mounted inertial measurement unit for pedestrian motion classification using continuous hidden Markov model. In: 2012 IEEE Int. Instrum. Meas. Technol. Conf. Proc., IEEE, pp 991–995.  https://doi.org/10.1109/I2MTC.2012.6229380
  27. He Y, Li Y, Bao S-D (2012) Fall detection by built-in tri-accelerometer of smartphone. In: Proc. 2012 IEEE-EMBS Int. Conf. Biomed. Heal. Informatics. IEEE, pp 184–187.  https://doi.org/10.1109/BHI.2012.6211540
  28. Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. Biomed Eng Online 12(1):66.  https://doi.org/10.1186/1475-925X-12-66 CrossRefGoogle Scholar
  29. Kang J, Yoo T, Kim H (2006) A Wrist–Worn integrated health monitoring instrument with a tele-reporting device for telemedicine and telecare. IEEE Trans Instrum Meas 55(5):1655–1661.  https://doi.org/10.1109/TIM.2006.881035 CrossRefGoogle Scholar
  30. Ketabdar H, Lyra M (2010) System and methodology for using mobile phones in live remote monitoring of physical activities. In: 2010 IEEE Int. Symp. Technol. Soc., IEEE, pp 350–356.  https://doi.org/10.1109/ISTAS.2010.5514619
  31. Khan MS, Yu M, Feng P, Wang L, Chambers J (2015) An unsupervised acoustic fall detection system using source separation for sound interference suppression. Signal Process 110:199–210.  https://doi.org/10.1016/j.sigpro.2014.08.021 CrossRefGoogle Scholar
  32. Li H, Shrestha A, Fioranelli F, Le Kernec J, Heidari H, Pepa M, Cippitelli E, Gambi E, Spinsante S (2017) Multisensor data fusion for human activities classification and fall detection. In: Proc IEEE Sensors, pp 1–3.  https://doi.org/10.1109/ICSENS.2017.8234179
  33. National Institute for Health and Clinical Excellence (2013) Falls in older people: assessing risk and prevention. National Institute for Health and Clinical ExcellenceGoogle Scholar
  34. Pazhoumand-Dar H (2019) Fame-adl: a data-driven fuzzy approach for monitoring the adls of elderly people using kinect depth maps. J Ambient Intell Hum Comput 10(7):2781–2803.  https://doi.org/10.1007/s12652-018-0990-1 CrossRefGoogle Scholar
  35. Public Health Agency of Canada (2014) Seniors’ Falls in Canada: second report, CanadaGoogle Scholar
  36. Rescio G, Leone A, Siciliano P (2013) Supervised expert system for wearable mems accelerometer-based fall detector. J Sens 2013:1–11.  https://doi.org/10.1155/2013/254629 CrossRefGoogle Scholar
  37. Roy N, Misra A, Cook D (2016) Ambient and smartphone sensor assisted adl recognition in multi-inhabitant smart environments. J Ambient Intell Hum Comput 7(1):1–19.  https://doi.org/10.1007/s12652-015-0294-7 CrossRefGoogle Scholar
  38. Sathyanarayana S, Satzoda RK, Sathyanarayana S, Thambipillai S (2018) Vision-based patient monitoring: a comprehensive review of algorithms and technologies. J Ambient Intell Hum Comput 9(2):225–251.  https://doi.org/10.1007/s12652-015-0328-1 CrossRefGoogle Scholar
  39. Tacconi C, Mellone S, Chiari L (2011) Smartphone-based applications for investigating falls and mobility. In: Proc. 5th Int. ICST Conf. Pervasive Comput. Technol. Healthc., IEEE.  https://doi.org/10.4108/icst.pervasivehealth.2011.246060
  40. Terroso M, Rosa N, Torres Marques A, Simoes R (2014) Physical consequences of falls in the elderly: a literature review from 1995 to 2010.  https://doi.org/10.1007/s11556-013-0134-8 CrossRefGoogle Scholar
  41. Tolkiehn M, Atallah L, Lo B, Yang G (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society, pp 369–372.  https://doi.org/10.1109/IEMBS.2011.6090120
  42. Vo QV, Lee G, Choi D (2012) Fall detection based on movement and smart phone technology. In: 2012 IEEE RIVF Int. Conf. Comput. Commun. Technol. Res. Innov. Vis. Futur., IEEE, pp 1–4.  https://doi.org/10.1109/rivf.2012.6169847

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of CataniaCataniaItaly

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