Comparison of low-complexity fall detection algorithms for body attached accelerometers.

The elderly population is growing rapidly. Fall related injuries are a central problem for this population. Elderly people desire to live at home, and thus, new technologies, such as automated fall detectors, are needed to support their independence and security. The aim of this study was to evaluate different low-complexity fall detection algorithms, using triaxial accelerometers attached at the waist, wrist, and head. The fall data were obtained from standardized types of intentional falls (forward, backward, and lateral) in three middle-aged subjects. Data from activities of daily living were used as reference. Three different detection algorithms with increasing complexity were investigated using two or more of the following phases of a fall event: beginning of the fall, falling velocity, fall impact, and posture after the fall. The results indicated that fall detection using a triaxial accelerometer worn at the waist or head is efficient, even with quite simple threshold-based algorithms, with a sensitivity of 97-98% and specificity of 100%. The most sensitive acceleration parameters in these algorithms appeared to be the resultant signal with no high-pass filtering, and the calculated vertical acceleration. In this study, the wrist did not appear to be an applicable site for fall detection. Since a head worn device includes limitations concerning usability and acceptance, a waist worn accelerometer, using an algorithm that recognizes the impact and the posture after the fall, might be optimal for fall detection.

[1]  Laura A Talbot,et al.  Falls in young, middle-aged and older community dwelling adults: perceived cause, environmental factors and injury , 2005, BMC public health.

[2]  Mohan Karunanithi,et al.  An observational trial of ambulatory monitoring of elderly patients , 2005 .

[3]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  W C Hayes,et al.  Disturbance type and gait speed affect fall direction and impact location. , 2001, Journal of biomechanics.

[5]  S. Brownsell,et al.  Do community alarm users want telecare? , 2000, Journal of telemedicine and telecare.

[6]  M. Hawley,et al.  Automatic fall detectors and the fear of falling , 2004, Journal of telemedicine and telecare.

[7]  C. Becker,et al.  Evaluation of a fall detector based on accelerometers: A pilot study , 2005, Medical and Biological Engineering and Computing.

[8]  Heinz Jäckel,et al.  SPEEDY:a fall detector in a wrist watch , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[9]  M. Kangas,et al.  Determination of simple thresholds for accelerometry-based parameters for fall detection , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  M. Prado,et al.  Preliminary evaluation of a full-time falling monitor for the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  S. Kivelä,et al.  Incidence rate of falls in an aged population in northern Finland. , 1994, Journal of clinical epidemiology.

[12]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[13]  A K Bourke,et al.  Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. , 2007, Gait & posture.

[14]  M N Nyan,et al.  Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. , 2006, Medical engineering & physics.

[15]  Koji Shibuya,et al.  Portable physical activity monitoring system for the evaluation of activity of the aged in daily life , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[16]  R. Halfens,et al.  Falls in German in-patients and residents over 65 years of age. , 2007, Journal of clinical nursing.

[17]  Timo Jämsä,et al.  A Device for Long Term Monitoring of Impact Loading on the Hip , 2003 .

[18]  H.C. Kim,et al.  Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Hulya,et al.  Bmc Public Health the Relationship between Risk Factors for Falling and the Quality of Life in Older Adults , 2022 .

[20]  A McIntosh,et al.  The design of a practical and reliable fall detector for community and institutional telecare , 2000, Journal of telemedicine and telecare.

[21]  G M Lyons,et al.  Long-term mobility monitoring of older adults using accelerometers in a clinical environment , 2004, Clinical rehabilitation.

[22]  Merryn J Mathie,et al.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement , 2004, Physiological measurement.