Multi-sensor acceleration-based action recognition

Abstract

In this paper, a framework to recognize human actions from acceleration data is proposed. An important step for an accurate recognition is the preprocessing of input data and the following classification by the machine learning algorithm. In this paper, we suggest to combine Dynamic Time Warping (DTW) with Random Forest. The intention of using DTW is to pre-process the data to eliminate outliers and to align the time series. Many applications require more than one inertial sensor for an accurate prediction of actions. In this paper, nine inertial sensors are deployed to ensure an accurate recognition of actions. Further, sensor fusion approaches are introduced and the most promising strategy is shown. The proposed framework is evaluated on a self-recorded dataset consisting of six human actions. Each action was performed three times by 20 subjects. The dataset is publicly available for download.