Multimode Heterogeneous Collaborative Sensing Method for Parkinson's Disease
YANG Xiao-dong1,2,3,4, CHEN Yi-qiang1,2,3, YU Han-chao1,3,4,6, ZHANG Ying-wei1,2,3,4, ZHONG Xi1,2,3,4, HU Zi-ang1,2,3,4, LIU Hong5,6
1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;
4. Beijing Key Laboratory for Parkinson's Disease, Beijing 100053, China;
5. School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong 250358, China;
6. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technonogy, Jinan, Shandong 250014, China
Abstract：Using multimodal heterogeneous sensors to form body sensing networks (BSNs) is one of the most important ways to continuously sensing users' daily activities,but high energy consumption is the main reason for restricting its development.This paper presents a multimode heterogeneous collaborative sensing method for Parkinson's disease to reduce the energy consumption in sensing the daily activities by BSNs.The proposed method divides activity recognition into two sub-tasks which contain activity detection and status monitoring.And it uses one multi-classifier to model activity detection task and several binary classifiers to model status monitoring tasks,which are based on the chosen optimal sensor sets.Experimental results on two public dataset show that comparing with the conventional method whose sensors run all the time,the energy consumption on data transportation and model computation is reduced by 40% in MHEALTH and 15% in PAMPA2 approximately without losing activity-sensing accuracy.Thus it can help extend the lifetime of BSNs to sense users' daily activities long-termly and continuously.
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2018, Vol. 46 
