Файл:Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition Abcbb87d8ff48ee47d446411a3455451f25b.pdf

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Deep_Convolutional_Neural_Networks_On_Multichannel_Time_Series_For_Human_Activity_Recognition_Abcbb87d8ff48ee47d446411a3455451f25b.pdf(0 × 0 пикселей, размер файла: 115 КБ, MIME-тип: application/pdf)

JianBoYang,MinhNhutNguyen,PhyoPhyoSan,XiaoLiLi,ShonaliKrishnaswamy Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore 138632 {yang-j,mnnguyen,sanpp,xlli,spkrishna}@i2r.a-star.edu.sg

Abstract

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective 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 systematic 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 discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

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