Statistical Complexity Measure Analysis Of Gait Signal Based On LMCD And JSD

When people are walking, they will produce gait signal. People of different ages, personality,occupation and health status will produce different gait signal. With the progress of science andtechnology and social development, the power of analyzing and processing gait signal has beengreatly improved. Gait signal is also becoming a common method in biomedical applications and ithelps to diagnose the disease and assess health status. In clinical practice, the study of gait signal isgood to assess the presence of the patients’s abnormal gait and provide the best means of evaluation.So it is of great significance for the rehabilitation and treatment of patients.Lopez-Mancini-Calbet Divergence and Jensen-Shannon Divergence are two importantdifferences. Firstly, the article described the formula of the LMC difference and Jensen Shannondifference. Secondly, this paper described the complexity’s specific algorithm of gait signal basedon the LMCD and JSD. The algorithmes included the symbolic processing of the the gait signaltime sequence, using B-P algorithm to statistics time series’s probability distribution and calculatingthe unbalanced items and information entropy. Finally, we calculated the complexity of gait signalof Parkinson’s patients, the elderly and young people by using the statistics complexity analysismethod based on LMCD and JSD. Then we detected the experimental data by variance detection.The results showed that: the complexity of the young people is maximum, then is the elderlypeople’s and the complexity of the patients with Parkinson is minimum. And the complexity’sdifference of the three gait signal varied greatly.Through the study of this article, we have got the range of gait signal complexity of Parkinson’spatients, the elderly and young people which is good for the clinical diagnosis. We draw aconclusion that the statistical complexity analysis method based on LMCD is better.