Article

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.
CI - Copyright (c) 2013 Elsevier B.V. All rights reserved.

Langue : ANGLAIS