Probabilistic Information Structure of Human Walking

Recently, the area of healthcare has been tremendously benefited from the advent of high performance computing in improving quality of life. Different processing techniques have been developed to understand the hidden complexity of the time series and will help clinicians in diagnosis and treatment. Analysis of human walking helps to study the various pathological conditions affecting balance and the elderly. In an elderly subjects, falls and paralysis are major problems, in terms of both frequency and consequences. Correct postural balance is important to well being and its effects will be felt in every movement and activity. In this paper, Bayesian Network (BN) was applied to recorded muscle activities and joint motions during walking, to extract causal information structure of normal walking and different impaired walking. The aim of this study is to use different BNs to express normal walking and various impaired walking, and identify the most important causal pairs that characterize specific impaired walking, through comparing the BNs for different walking.