Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks

作者：Aleksandar Vakanski, Jake M. Ferguson, Lee S · 发表于：Journal of Physiotherapy & Physical Rehabilitation · 年份：2016 · DOI：10.4172/2573-0312.1000118 · 被引用次数：34 · 研究领域：Human Pose and Action Recognition、Balance, Gait, and Falls Prevention、Stroke Rehabilitation and Recovery

OBJECTIVE: The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement. METHODS: The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. RESULTS: The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-lik...