A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.
Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.
Injury Prevention and Mobility Laboratory, Simon Fraser University, Burnaby, BC, Canada. oaziz@sfu.ca.|School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada. oaziz@sfu.ca.|School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada. oaziz@sfu.ca.
Omar
O
Aziz
School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada.
Magnus
M
Musngi
School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada.
Edward J
EJ
Park
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
Greg
G
Mori
Injury Prevention and Mobility Laboratory, Simon Fraser University, Burnaby, BC, Canada.|Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.|School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
Stephen N
SN
Robinovitch
D061725:Accelerometry; D000058:Accidental Falls; D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D006801:Humans; D000069550:Machine Learning; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
Falls; Machine learning; Older adults; Threshold-based algorithms; Wearable sensors