Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities

• Published in: Journal of biomechanics Vol. 43; no. 15; pp. 3051 - 3057
• Main Authors: Bourke, A.K, van de Ven, P, Gamble, M, O’Connor, R, Murphy, K, Bogan, E, McQuade, E, Finucane, P, ÓLaighin, G, Nelson, J
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 16.11.2010; Elsevier; Elsevier Limited
• Subjects: Acceleration; Accelerometer; Accelerometers; Accidental Falls - prevention & control; Activities of Daily Living; ADL; Adult; Aged; Aged, 80 and over; Algorithms; Arrivals; Biological and medical sciences; Biomechanical Phenomena; Biomechanics; Biomedical Engineering - instrumentation; Biomedical Engineering - statistics & numerical data; Databases, Factual; Devices; Fall profile; Fall-detection; Falls in the elderly; False Positive Reactions; Female; Fundamental and applied biological sciences. Psychology; Humans; Impact; Male; Models, Biological; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - statistics & numerical data; Mortality; Physical Medicine and Rehabilitation; Postural Balance - physiology; Posture; Sum-vector signal; Thresholds; Velocity; Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports; Young Adult; Impact; Accelerometer; ADL; Falls in the elderly; Fall-detection; Sum-vector signal; Fall profile; Velocity; Posture; Human; Performance evaluation; Daily living; Algorithm; Young adult; Signal processing; Fall; Detection; Elderly; Biomedical engineering
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2010.07.005

Abstract It is estimated that by 2050 more than one in five people will be aged 65 or over. In this age group, falls are one of the most serious life-threatening events that can occur. Their automatic detection would help reduce the time of arrival of medical attention, thus reducing the mortality rate and in turn promoting independent living. This study evaluated a variety of existing and novel fall-detection algorithms for a waist-mounted accelerometer based system. In total, 21 algorithms of varying degrees of complexity were tested against a comprehensive data-set recorded from 10 young healthy volunteers performing 240 falls and 120 activities of daily living (ADL) and 10 elderly healthy volunteers performing 240 scripted ADL and 52.4 waking hours of continuous unscripted normal ADL. Results show that using an algorithm that employs thresholds in velocity, impact and posture (velocity+impact+posture) achieves 100% specificity and sensitivity with a false-positive rate of less than 1 false-positive (0.6 false-positives) per day of waking hours. This algorithm is the most suitable method of fall-detection, when tested using continuous unscripted activities performed by elderly healthy volunteers, which is the target environment for a fall-detection device.