Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined hu-man activities. In this problem, extracting effec-tive features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a sys-tematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discrimi-native power. Unified in one model, feature learn-ing and classification are mutually enhanced. All these unique advantages of the CNN make it out-perform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recogni-tion Challenge and other benchmark datasets