Advertisement

Aging Clinical and Experimental Research

, Volume 30, Issue 11, pp 1275–1286 | Cite as

Falls management framework for supporting an independent lifestyle for older adults: a systematic review

  • Hoa NguyenEmail author
  • Farhaan Mirza
  • M. Asif Naeem
  • Mirza Mansoor Baig
Review

Abstract

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults’ independent living and well-being.

Keywords

Falls detection Falls prediction Falls prevention Falls management Internet of things (IoT) Falls management framework and older adult falls 

Notes

Compliance with ethical standards

Conflict of interest

Authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.

References

  1. 1.
    Ageing W, Unit L (2008) WHO global report on falls prevention in older age. World Health Organization, GenevaGoogle Scholar
  2. 2.
    United Nations, Department of Economic and Social Affairs, Population Division (2015) World Population Ageing 2015Google Scholar
  3. 3.
    Kannus P et al (2005) Prevention of falls and consequent injuries in elderly people. Lancet 366:1885–1893CrossRefGoogle Scholar
  4. 4.
    Vellas B et al (1987) Prospective study of restriction of acitivty in old people after falls. Age Ageing 16:189–193CrossRefGoogle Scholar
  5. 5.
    Moher D et al (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8:336–341CrossRefGoogle Scholar
  6. 6.
    He J, Hu C, Wang X (2016) A smart device enabled system for autonomous fall detection and alert. Int J Distrib Sens Netw 12:2308183CrossRefGoogle Scholar
  7. 7.
    Kau L-J, Chen C-S (2015) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Health Inform 19:44–56CrossRefGoogle Scholar
  8. 8.
    Hsieh C-Y et al (2017) Novel hierarchical fall detection algorithm using a multiphase fall model. Sensors 17:307CrossRefGoogle Scholar
  9. 9.
    Pierleoni P et al (2015) A high reliability wearable device for elderly fall detection. IEEE Sens J 15:4544–4553CrossRefGoogle Scholar
  10. 10.
    Cheng AL, Georgoulas C, Bock T (2016) Fall detection and intervention based on wireless sensor network technologies. Autom Constr 71:116–136CrossRefGoogle Scholar
  11. 11.
    de Miguel K et al (2017) Home camera-based fall detection system for the elderly. Sensors 17:2864CrossRefGoogle Scholar
  12. 12.
    Liu L et al (2016) An automatic in-home fall detection system using Doppler radar signatures. J Ambient Intell Smart Environ 8:453–466CrossRefGoogle Scholar
  13. 13.
    Juang L-H, Wu M-N (2015) Fall down detection under smart home system. J Med Syst 39:107CrossRefGoogle Scholar
  14. 14.
    De Backere F et al (2015) Towards a social and context-aware multi-sensor fall detection and risk assessment platform. Comput Biol Med 64:307–320CrossRefGoogle Scholar
  15. 15.
    Zerrouki N et al (2016) Accelerometer and camera-based strategy for improved human fall detection. J Med Syst 40:284CrossRefGoogle Scholar
  16. 16.
    Kwolek B, Kepski M (2016) Fuzzy inference-based fall detection using kinect and body-worn accelerometer. Appl Soft Comput 40:305–318CrossRefGoogle Scholar
  17. 17.
    Horta ET, Lopes IC, Rodrigues JJ (2015) Ubiquitous mHealth approach for biofeedback monitoring with falls detection techniques and falls prevention methodologies. In: Mobile health. Springer, Cham, pp 43–75Google Scholar
  18. 18.
    Khan SS et al (2017) Detecting falls with X-factor hidden Markov models. Appl Soft Comput 55:168–177CrossRefGoogle Scholar
  19. 19.
    Kim T et al (2017) Characterizing dynamic walking patterns and detecting falls with wearable sensors using Gaussian process methods. Sensors 17:1172CrossRefGoogle Scholar
  20. 20.
    Mao A et al. (2017) Highly portable, sensor-based system for human fall monitoring. Sensors, 17:2096CrossRefGoogle Scholar
  21. 21.
    Shen R-K et al (2017) A novel fall prediction system on smartphones. IEEE Sens J 17:1865–1871CrossRefGoogle Scholar
  22. 22.
    He J, Bai S, Wang X (2017) An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network Classifier. Sensors 17:1393CrossRefGoogle Scholar
  23. 23.
    Hamm J et al (2017) Mobile three-dimensional visualisation technologies for clinician-led fall prevention assessments. Health Inform J.  https://doi.org/10.1177/1460458217723170 CrossRefGoogle Scholar
  24. 24.
    Lin T-H, Yang C-Y, Shih W-P (2017) Fall prevention shoes using camera-based line-laser obstacle detection system. J Healthc Eng 2017:8264071.  https://doi.org/10.1155/2017/8264071 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Rantz M et al (2015) Automated in-home fall risk assessment and detection sensor system for elders. Gerontologist 55(Suppl_1):S78–S87CrossRefGoogle Scholar
  26. 26.
    Wang H et al (2016) RT-fall: a real-time and contactless fall detection system with commodity WiFi devices. IEEE Trans Mobile Comput 16:511–526CrossRefGoogle Scholar
  27. 27.
    Wang Y, Wu K, Ni LM (2016) Wifall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16:581–594CrossRefGoogle Scholar
  28. 28.
    Baig MM, Gholamhosseini H, Connolly MJ (2016) Falls risk assessment for hospitalised older adults: a combination of motion data and vital signs. Aging Clin Exp Res 28:1159–1168CrossRefGoogle Scholar
  29. 29.
    Danielsen A, Olofsen H, Bremdal BA (2016) Increasing fall risk awareness using wearables: a fall risk awareness protocol. J Biomed Inform 63:184–194CrossRefGoogle Scholar
  30. 30.
    Nizam Y, Mohd MNH, Jamil MMA (2016) A study on human fall detection systems: daily activity classification and sensing techniques. Int J Integr Eng 8:35–43Google Scholar
  31. 31.
    Naschitz JE, Rosner I (2007) Orthostatic hypotension: framework of the syndrome. Postgrad Med J 83:568–574CrossRefGoogle Scholar
  32. 32.
    Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Programs Biomed 117:489–501CrossRefGoogle Scholar
  33. 33.
    Oliver D (2008) Falls risk-prediction tools for hospital inpatients. Time to put them to bed? Age ageing 37:248–250CrossRefGoogle Scholar
  34. 34.
    Chao Y-Y et al (2013) The feasibility of an intervention combining self-efficacy theory and Wii Fit exergames in assisted living residents: a pilot study. Geriatr Nurs 34:377–382CrossRefGoogle Scholar
  35. 35.
    Pisan Y, Marin JG, Navarro KF (2013) Improving lives: using Microsoft Kinect to predict the loss of balance for elderly users under cognitive load. In: Proceedings of the 9th Australasian conference on interactive entertainment: matters of life and death, ACMGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hoa Nguyen
    • 1
    Email author
  • Farhaan Mirza
    • 1
  • M. Asif Naeem
    • 1
  • Mirza Mansoor Baig
    • 1
  1. 1.School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations