A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data

TLDR

Human activity monitoring is increasingly important due to sedentary lifestyles, and prior work has used various classification schemes and feature extraction methods on diverse datasets. The study compares 14 feature extraction methods for classifying dynamic activities from accelerometer signals. We evaluated these methods—wavelet, time‑ and frequency‑domain features—using two datasets from 20 subjects, assessing classification accuracy across three accelerometer placements with nearest‑neighbor classifiers and robust subject‑based cross‑validation. Frequency‑based features outperformed wavelet‑based ones, with the best feature sets achieving over 95 % intersubject classification accuracy.

Abstract

Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.