title: Selecting Power-Efficient Signal Features for a Low-Power Fall Detector.
abstract: Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.
authors.affiliation: Graduate School of Biomedical EngineeringUniversity of New South Wales.
authors.forename: Changhong
authors.initials: C
authors.lastname: Wang
authors.affiliation: Graduate School of Biomedical EngineeringUniversity of New South Wales.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.affiliation: Graduate School of Biomedical EngineeringUniversity of New South Wales.
authors.forename: Wei
authors.initials: W
authors.lastname: Lu
authors.affiliation: Graduate School of Biomedical EngineeringUniversity of New South Wales.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Stevens
authors.affiliation: Neuroscience Research AustraliaUniversity of New South Wales.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Lord
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Kensington, N.S.W., Australia.
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D000058:Accidental Falls / Q000517:prevention & control*; D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D000076251:Wearable Electronic Devices
keywords: Acceleration; Accelerometers; Batteries; Detection algorithms; Detectors; Feature extraction; Neon