A Hierarchical Meta-Classifier for Human Activity Recognition

Abstract

This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxial accelerometer. Time and frequency based features were extracted from two-second windows of raw accelerometer data and a subset of the features, together with demographic information, was selected for classification. The activities were divided into five activity groups: non-ambulatory activities, walking, running, climbing upstairs, and climbing downstairs. Multiple classification techniques were tested for each classifier level and groups. Random forests were found to perform comparatively better at each level. Based upon those tests, a 3-level hierarchical classifier, consisting of 5 random forest classifiers, was built. At the first level, the non-ambulatory activities are separated from the rest. At the second, the ambulatory activities are divided into four activity groups. At the final level, the activities are classified individually. Accuracy on test sets was found to be approximately 87% overall for individual activities and 94% at the activity group level. These results compare favorably to contemporary results in classifying human activity.