title: Can we use accelerometry to monitor balance exercise performance in older adults?
abstract: While home-based balance exercises are recommended to reduce the risk of falling and fractures in older adults, adherence to exercise remains suboptimal. The long-term objective of this research is to advance body-worn sensor techniques to measure at-home exercise performance and promote adherence. In this study, a method of distinguishing 5 types of walking using hip- and ankle-worn accelerometers was developed and evaluated in a target clinical population. A secondary objective was to evaluate the method's sensitivity to sensor placement. Eighteen community-dwelling, older females (≥50 years) with low bone mass wore triaxial accelerometers at the left hip and each ankle while performing 5 walking tasks at home: 4 walking balance exercises (figure 8, heel-toe, sidestep, backwards) and straight-line walking. Sensor data were separated into low (0.5-2 Hz) and high (2-10 Hz) frequency bands, and root-mean-square values (energy) were computed for each sensor, axis, and band. These 18 energy estimates were used as inputs to a neural network classifier with 5 outputs, corresponding to each task. Using a leave-one-out cross-validation protocol, the neural network correctly classified 82/90 test instances (91% accuracy). Compared to random selection accuracy of 20% (i.e., 1 in 5), the results indicated excellent separation between tasks. Reducing the sensor set to one hip and one ankle resulted in 6.7-8.9% reduction in accuracy. Our findings can be used in the development of tools used to deliver exercise performance metrics (e.g., % completed) or recognize walking and balance exercise activities using body-worn accelerometers.
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; David Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. Electronic address: james.tung@uwaterloo.ca.
authors.forename: James Y
authors.initials: JY
authors.lastname: Tung
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
authors.forename: Helen
authors.initials: H
authors.lastname: Ng
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
authors.forename: Cameron
authors.initials: C
authors.lastname: Moore
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada.
authors.forename: Lora
authors.initials: L
authors.lastname: Giangregorio
mesh_terms: D061725:Accelerometry; D000368:Aged; D000465:Algorithms; D015519:Bone Density; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D015663:Osteoporosis, Postmenopausal / Q000503:physiopathology* / Q000534:rehabilitation; D004856:Postural Balance / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D011795:Surveys and Questionnaires; D016138:Walking / Q000502:physiology*
keywords: Balance exercise; Falls; Fractures; Low bone mass; Osteoporosis