Paper Info
Title
Inferring Cognitive Wellness from Motor Patterns.
Abstract
Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinsonu0027s disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing peopleu0027s 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 peopleu0027s 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.
Year
Venue
Field
2018
IEEE Trans. Knowl. Data Eng.
Data mining,Feature vector,Feature selection,Computer science,Extreme learning machine,Support vector machine,Feature extraction,Artificial intelligence,Overfitting,Cognition,Random forest,Machine learning
DocType
Volume
Issue
Journal
30
12
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Yiqiang Chen11446109.32
Chunyu Hu2182.42
Bin Hu3778107.21
Lisha Hu41037.45
Han Yu563948.71
Chunyan Miao62307195.72