Article

The purpose of this study was to develop a new algorithm for classifying physical
activity into either locomotive or household activities using a triaxial
accelerometer. Sixty-six volunteers (31 men and 35 women) participated in this
study and were separated randomly into validation and cross-validation groups.
All subjects performed 12 physical activities (personal computer work, laundry,
dishwashing, moving a small load, vacuuming, slow walking, normal walking, brisk
walking, normal walking while carrying a bag, jogging, ascending stairs and
descending stairs) while wearing a triaxial accelerometer in a controlled
laboratory setting. Each of the three signals from the triaxial accelerometer was
passed through a second-order Butterworth high-pass filter to remove the
gravitational acceleration component from the signal. The cut-off frequency was
set at 0.7 Hz based on frequency analysis of the movements conducted. The ratios
of unfiltered to filtered total acceleration (TAU/TAF) and filtered vertical to
horizontal acceleration (VAF/HAF) were calculated to determine the cut-off value
for classification of household and locomotive activities. When the TAU/TAF
discrimination cut-off value derived from the validation group was applied to the
cross-validation group, the average percentage of correct discrimination was
98.7%. When the VAF/HAF value similarly derived was applied to the
cross-validation group, there was relatively high accuracy but the lowest
percentage of correct discrimination was 63.6% (moving a small load). These
findings suggest that our new algorithm using the TAU/TAF cut-off value can
accurately classify household and locomotive activities.
CI - Copyright 2010 Elsevier B.V. All rights reserved.

Langue : ANGLAIS