Automatic Recognition of Gait Phases Using a Multiple-Regression Hidden Markov Model

This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a multiple-regression hidden Markov model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem, in which each phase, called a segment, is modeled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labeling, which is a laborious time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall, and precision. Experiments conducted with five subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture model, k-means, and hidden Markov model), while remaining competitive with respect to standard supervised classification methods (support vector machine, random forest, and k-nearest neighbor).