Classifying household and locomotive activities using a triaxial accelerometer

Gait Posture. 2010 Mar;31(3):370-4. doi: 10.1016/j.gaitpost.2010.01.005. Epub 2010 Feb 6.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Acceleration
  • Activities of Daily Living / classification*
  • Adult
  • Algorithms*
  • Body Height
  • Body Weight
  • Female
  • Humans
  • Locomotion / physiology*
  • Male
  • Monitoring, Physiologic / instrumentation*
  • Posture / physiology
  • ROC Curve
  • Sensitivity and Specificity