A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor

• Published in: Medical engineering & physics Vol. 30; no. 1; pp. 84 - 90
• Main Authors: Bourke, A.K., Lyons, G.M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2008
• Subjects: Accidental Falls; Activities of Daily Living; Adult; Aged; Aged, 80 and over; Algorithms; Fall detection; Falls in the elderly; Gait; Gyroscope; Humans; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Movement; Postural Balance; Posture; Radiology; Reference Standards; Rotation; Sensitivity and Specificity; Threshold; Transducers; Activities of Daily Living; Gyroscope; Falls in the elderly; Fall detection; Threshold
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2006.12.001

A threshold-based algorithm, to distinguish between Activities of Daily Living (ADL) and falls is described. A gyroscope based fall-detection sensor array is used. Using simulated-falls performed by young volunteers under supervised conditions onto crash mats and ADL performed by elderly subjects, the ability to discriminate between falls and ADL was achieved using a bi-axial gyroscope sensor mounted on the trunk, measuring pitch and roll angular velocities, and a threshold-based algorithm. Data analysis was performed using M atlab ® to determine the angular accelerations, angular velocities and changes in trunk angle recorded, during eight different fall and ADL types. Three thresholds were identified so that a fall could be distinguished from an ADL: if the resultant angular velocity is greater than 3.1 rads/s (Fall Threshold 1), the resultant angular acceleration is greater than 0.05 rads/s 2 (Fall Threshold 2), and the resultant change in trunk-angle is greater than 0.59 rad (Fall Threshold 3), a fall is detected. Results show that falls can be distinguished from ADL with 100% accuracy, for a total data set of 480 movements.