Human Movement Analysis: Extension of the F-Statistic to Time Series using HMM

Abstract—Optical motion tracking has enhanced human move-ment analysis in medicine, biomechanics, and rehabilitation science by providing highly accurate joint angle measurements over time. However, analyzing the large amount of recorded data is challenging. The process is usually simplified by calculating descriptive measures, such as the minimum, mean, or maximum, from the time series data. We propose a novel technique for the analysis of human motion data, which considers the complete time series data and is based on the F-statistic traditionally used in medical and biomechanical studies. The time series data is modeled by a Hidden Markov Model (HMM) and the F-statistic is reformulated using the Kullback-Leibler divergence for comparing HMMs. This provides a novel technique to enhance the analysis of human movement data and includes an automatic generation of group-specific trajectories to simplify visual data analysis. It is further suitable as time-series based, univariate feature selection technique in machine learning