Comprehensive assessment of gait signals using multiple time scale features

Abstract

It is a challenging problem to detect and analyze gait signals for health evaluation. In this article, we propose a comprehensive assessment method using multiple time scale features to extract gait signal characteristics. Multi-resolution wavelet transform, together with logic regression and correlation analysis, was adapted for statistical analysis. The results show that the primary period and autocorrelation of gait signals vary substantially in three cohorts of people, namely normal young people, healthy old people and those with Parkinson's diseases. Furthermore, it is found that there is a correlation between the periodicity of gait sequences and the degree of Parkinson's diseases. In conclusion, these multiple scale features are very useful for health evaluation.