Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139110
Title: Inferring cognitive wellness from motor patterns
Authors: Chen, Yiqiang
Hu, Chunyu
Hu, Bin
Hu, Lisha
Yu, Han
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Chen, Y., Hu, C., Hu, B., Hu, L., Yu, H., & Miao, C. (2018). Inferring cognitive wellness from motor patterns. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2340-2353. doi:10.1109/tkde.2018.2820024
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: 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 outperform 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.
URI: https://hdl.handle.net/10356/139110
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2820024
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2820024
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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