Inferring Cognitive Wellness from Motor Patterns

doi:10.1109/TKDE.2018.2820024

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Inferring Cognitive Wellness from Motor Patterns
	Hu, Lisha 4,5; Hu, Bin 3; Hu, Chunyu 4,5; Chen, Yiqiang 4,5; Miao, Chunyan 1,2; Yu, Han 1,2
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2018-12-01
卷号	30 期号:12 页码:2340-2353
关键词	Correlation analysis motor pattern cognitive wellness imbalanced small-sampling feature selection imbalanced classification
ISSN号	1041-4347
DOI	10.1109/TKDE.2018.2820024
英文摘要	Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly out perform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure.
资助项目	National Key Research and Development Program of China[2017YFB1002801] ; Natural Science Foundation of China[61572471] ; Natural Science Foundation of China[61502456] ; Science and Technology Planning Project of Guangdong Province[2015B010105001] ; National Research Foundation, Prime Minister's Office, Singapore ; Nanyang Assistant Professorship, Nanyang Technological University ; Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project)[MOH/NIC/COG04/2017]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000450158600009
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4341]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yiqiang
作者单位	1.Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore 639798, Singapore 2.Nanyang Technol Univ, SCSE, Singapore 639798, Singapore 3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China 4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Hu, Lisha,Hu, Bin,Hu, Chunyu,et al. Inferring Cognitive Wellness from Motor Patterns[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2018,30(12):2340-2353.
APA	Hu, Lisha,Hu, Bin,Hu, Chunyu,Chen, Yiqiang,Miao, Chunyan,&Yu, Han.(2018).Inferring Cognitive Wellness from Motor Patterns.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,30(12),2340-2353.
MLA	Hu, Lisha,et al."Inferring Cognitive Wellness from Motor Patterns".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 30.12(2018):2340-2353.