Skip to main content
SpringerLink
Log in

Technological Assistance for Fall Among Aging Population: A Review

  • Conference paper
  • First Online:
Research into Design for Communities, Volume 1 (ICoRD 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 65))

Included in the following conference series:

Abstract

Recent studies report that globally unintentional fall among aging population is one of the most costly and complex healthcare issue. There is a possibility that elderly might fall when they are alone and no one is there to help them. In such conditions if the fall remained unnoticed for a long duration its impact can be fatal. To avoid any severe after- fall damage there are various technology based solutions available which can reduce such issues. These include fall detection system which can be Video Based, Environmental Sensor Based, Wearable and Mixed Approach, i.e., combination of two or more techniques. It assists elderly and their caregivers through detecting falls and calling for help as soon as falls occur via triggering notification alarms. This paper gives a review for technologies related to fall detection for aging people and their caregivers along with suggestions for future research directions. Cost effectiveness, privacy, perceived usefulness and ease of technology used are the important factors for a successful technology intervention.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rao, S.S.: Prevention of falls in older patients. Am. Fam. Physician 72(1), 81–88 (2005)

    Google Scholar 

  2. Sudarshan, B.G., Hegde, R., SC, P.K., Satyanarayana, B.S.: Design and Development of Fall Detector Using Fall Acceleration

    Google Scholar 

  3. Le Deist, F., Latouille, M.: Acceptability Conditions for Telemonitoring Gerontechnology in the Elderly: Optimising the Development and Use of This New Technology. IRBM (2016)

    Google Scholar 

  4. Huang, C.C., Sun, C., Wu, T.L., Sheth, C.: A Pervasive Way: Elderly People Falling Detection and Ambient Intelligence. CHI (2010)

    Google Scholar 

  5. Hawley-Hague, H., Boulton, E., Hall, A., Pfeiffer, K., Todd, C.: Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review. Int. J. Med. Inf. 83(6), 416–426 (2014)

    Article  Google Scholar 

  6. Mollaret, C., Mekonnen, A.A., Lerasle, F., Ferrané, I., Pinquier, J., Boudet, B., Rumeau, P.: A multi-modal perception based assistive robotic system for the elderly. Comput. Vis. Image Underst. (2016)

    Google Scholar 

  7. Hamm, J., Money, A.G., Atwal, A., Paraskevopoulos, I.: Fall prevention intervention technologies: a conceptual framework and survey of the state of the art. J. Biomed. Inf. (2016)

    Google Scholar 

  8. El-Bendary, N., Tan, Q., Pivot, F.C., Lam, A.: Fall detection and prevention for the elderly: a review of trends and challenges. Int. J. Smart Sens. Intell. Syst. 6(3), 1230–1266 (2013)

    Google Scholar 

  9. Jeyalakshmi, S., Chakrabarti, S., Nivedita, G.: Situation analysis of the elderly in India, 2011 Central Statistics Office, Ministry of Statistics & Programme Implementation. Government of India

    Google Scholar 

  10. Rajan, S.I.: Population Ageing and Health in India (2006)

    Google Scholar 

  11. Plaza, I., MartíN, L., Martin, S., Medrano, C.: Mobile applications in an aging society: status and trends. J. Syst. Softw. 84(11), 1977–1988 (2011)

    Article  Google Scholar 

  12. Fisk, A.D., Rogers, W.A., Charness, N., Czaja, S.J., Sharit, J.: Designing for Older Adults: Principles and Creative Human Factors Approaches. CRC press (2009)

    Google Scholar 

  13. Kuo, M.H., Wang, S.L., Chen, W.T.: Using information and mobile technology improved elderly home care services. Health Policy Technol. (2016)

    Google Scholar 

  14. Ma, Q., Chan, A.H., Chen, K.: Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl. Ergon. 54, 62–71 (2016)

    Article  Google Scholar 

  15. Mathur, A.: Contemporary Issues in the Health of the Elderly, pp. 38–43. [Online]. http://www.apiindia.org/pdf/medicine_update_2007/7.pdf. Accessed 09 Oct 2014

  16. Dsouza, S.A., Rajashekar, B., Dsouza, H.S., Kumar, K.: Falls in Indian older adults: a barrier to active ageing. Asian J. Gerontol. Geriatr. 9(1), 1–8 (2014)

    Google Scholar 

  17. Jeyalakshmi, S., Chakrabarti, S., Nivedita, G.: Situation Analysis of The Elderly in India, 2011 Central Statistics Office, Ministry of Statistics & Programme Implementation. Government of India

    Google Scholar 

  18. Ye, Z., Li, Y., Zhao, Q., Liu, X.: A falling detection system with wireless sensor for the elderly people based on ergnomics. Int. J. Smart Home 8(1), 187–196 (2014)

    Article  Google Scholar 

  19. Kulkarni, S., Basu, M.: A review on wearable tri-axial accelerometer based fall detectors. J. Biomed. Eng. Technol. 1(3), 36–39 (2013)

    Google Scholar 

  20. Gasparrini, S., Cippitelli, E., Spinsante, S., Gambi, E.: A depth-based fall detection system using a Kinect® sensor. Sensors 14(2), 2756–2775 (2014)

    Google Scholar 

  21. Yang, L., Ren, Y., Zhang, W.: 3D depth image analysis for indoor fall detection of elderly people. Digital Commun. Netw. 2(1), 24–34 (2016)

    Article  Google Scholar 

  22. Gibson, R.M., Amira, A., Ramzan, N., Casaseca-de-la-Higuera, P., Pervez, Z.: Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl. Soft Comput. 39, 94–103 (2016)

    Article  Google Scholar 

  23. Saborowski, M., Kollak, I.: “How do you care for technology?”—Care professionals’ experiences with assistive technology in care of the elderly. Technol. Forecast. Soc. Chang. 93, 133–140 (2015)

    Article  Google Scholar 

  24. Liu, L., Popescu, M., Skubic, M., Rantz, M., Yardibi, T., Cuddihy, P.: Automatic fall detection based on Doppler radar motion signature. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 222–225 (2011)

    Google Scholar 

  25. Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28(2), 285–291 (2008)

    Article  Google Scholar 

  26. Bowen, M.E., Craighead, J., Wingrave, C.A., Kearns, W.D.: Real-time locating systems (RTLS) to improve fall detection. Gerontechnology 9(4), 464–471 (2010)

    Article  Google Scholar 

  27. Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., Vecchio, A.: A smartphone-based fall detection system. Pervasive Mobile Comput. 8(6), 883–899 (2012)

    Article  Google Scholar 

  28. Freitas, R., Terroso, M., Marques, M., Gabriel, J., Torres Marques, A., Simoes, R.: Wearable sensor networks supported by mobile devices for fall detection. In: Sensors 2014 IEEE, pp. 2246–2249 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Design, IIT Guwahati, Guwahati, Assam, India

    Nilakshi Yein & Swati Pal

Authors
  1. Nilakshi Yein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swati Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nilakshi Yein .

Editor information

Editors and Affiliations

  1. Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore, India

    Amaresh Chakrabarti

  2. Department of Design, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Debkumar Chakrabarti

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Yein, N., Pal, S. (2017). Technological Assistance for Fall Among Aging Population: A Review. In: Chakrabarti, A., Chakrabarti, D. (eds) Research into Design for Communities, Volume 1. ICoRD 2017. Smart Innovation, Systems and Technologies, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-3518-0_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3518-0_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3517-3

  • Online ISBN: 978-981-10-3518-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation