Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

  • Stefan Almer
  • Josef Kolbitsch
  • Johannes Oberzaucher
  • Martin Ebner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7383)


With an increasing population of older people the number of falls and fall-related injuries is on the rise. This will cause changes for future health care systems, and fall prevention and fall detection will pose a major challenge. Taking the multimodal character of fall-related parameters into account, the development of adequate strategies for fall prevention and detection is very complex. Therefore, it is necessary to collect and analyze fall-related data.

This paper describes the development of a test framework to perform a variety of assessment tests to collect fall-related data. The aim of the framework is to easily set up assessment tests and analyze the data regarding fall-related behaviors. It offers an open interface to support a variety of devices. The framework consists of a Web service, a relational database and a Web-based backend. In order to test the framework, a mobile device client recording accelerometer and gyroscope sensor data is implemented on the iOS platform. The evaluation, which includes three mobility assessment tests, demonstrates the sensor accuracy for movement analysis for further feature extraction.


fall detection fall prevention mobile devices restful Web service 


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  1. 1.
    Podsiadlo, D., Richardson, S.: The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons. American Geriatrics Society 39, 142–148 (1991)Google Scholar
  2. 2.
    Whitney, S.L., Wrisley, D.M., Marchetti, G.F., Gee, M.A., Redfern, M.S., Furman, J.M.: Clinical Measurement of Sit-To-Stand Performance in People With Balance Disorders: Validity of Data for the Five-Times-Sit-To-Stand Test. Physical Therapy 85, 1034–1045 (2005)Google Scholar
  3. 3.
    Lewis, C., Shaw, K.: Benefits of the 2-Minute Walk Test. Physical Therapy & Rehab Medicine 16 (2005)Google Scholar
  4. 4.
    Yu, X.: Approaches and Principles of Fall Detection for Elderly and Patient. In: 10th International Conference on e-Health Networking, Applications and Services, pp. 42–47. IEEE, Singapore (2008)Google Scholar
  5. 5.
    Lopes, I.C., Vaidya, B., Rodrigues, J.J.P.C.: SensorFall - An Accelerometer Based Mobile Application. In: 2nd International Conference on Computer Science and its Applications, pp. 1–6. IEEE, Jeju (2009)CrossRefGoogle Scholar
  6. 6.
    Dai, J., Bai, X., Yang, Z., Shen, Z., Xuan, D.: PerFallD: A Pervasive Fall Detection System using Mobile Phones. In: 8th IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 292–297. IEEE, Mannheim (2010)Google Scholar
  7. 7.
    Sposaro, F., Tyson, G.: iFall: An Android Application for Fall Monitoring and Response. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6119–6122. IEEE, Minneapolis (2009)CrossRefGoogle Scholar
  8. 8.
    van den Broek, G., Cavallo, F., Odetti, L., Wehrmann, C.: Ambient Assisted Living Roadmap. VDI/VDE-IT AALIANCE Office (2009)Google Scholar
  9. 9.
    Eurostat: Population Projections 2008-2060,
  10. 10.
    World Health Organization: WHO Global Report on Falls Prevention in Older Age. World Health Organization (2007)Google Scholar
  11. 11.
    Kangas, M., Konttila, A., Winblad, I., Jämsa, T.: Determination of Simple Thresholds for Accelerometry-Based Parameters for Fall Detection. In: 29th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 1367–1370. IEEE, Lyon (2007)CrossRefGoogle Scholar
  12. 12.
    Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G., Rialle, V., Lundy, J.E.: Fall Detection - Principles and Methods. In: 29th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 1663–1666. IEEE, Lyon (2007)CrossRefGoogle Scholar
  13. 13.
    Fielding, R.T.: Architectural Styles and the Design of Network-based Software Architectures. University of California, Irvine (2000)Google Scholar
  14. 14.
    Todd, C., Skelton, D.: What are the main risk factors for falls among older people and what are the most effective interventions to prevent these falls? WHO Regional Office for Europe, Copenhagen (2004)Google Scholar
  15. 15.
    Tremblay Jr., K.R., Barber, C.E.: Preventing Falls in the Elderly (2005),
  16. 16.
    LeMier, M., Silver, I., Bowe, C.: Falls Among Older Adults: Strategies for Prevention. Washington State Department of Health (2002)Google Scholar
  17. 17.
    Bridging Research in Ageing and ICT Development: Automatic Wearable Fall Detectors (2008),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefan Almer
    • 1
  • Josef Kolbitsch
    • 1
  • Johannes Oberzaucher
    • 2
  • Martin Ebner
    • 1
  1. 1.Institute for Information Systems and Computer MediaGraz University of TechnologyGrazAustria
  2. 2.Institute for Rehabilitation and Ambient Assisted Living TechnologiesCeit RaltecSchwechatAustria

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