Automatic Recognition of Gait phases Using a Multiple Regression Hidden Markov Model

Ferhat Attal 1 Yacine Amirat 1 Abdelghani Chibani 1 Samer Mohammed 1
1 SIRIUS
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : 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 modelled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labelling, 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 5 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 (GMM, k-Means and HMM) while remaining competitive with respect to standard supervised classification methods (SVM, RF and k-NN).
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IEEE/ASME Transactions on Mechatronics, Institute of Electrical and Electronics Engineers, 2018, 〈10.1109/TMECH.2018.2836934〉
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Contributeur : Lab Lissi <>
Soumis le : mardi 15 mai 2018 - 15:09:52
Dernière modification le : lundi 23 juillet 2018 - 12:42:04

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Ferhat Attal, Yacine Amirat, Abdelghani Chibani, Samer Mohammed. Automatic Recognition of Gait phases Using a Multiple Regression Hidden Markov Model. IEEE/ASME Transactions on Mechatronics, Institute of Electrical and Electronics Engineers, 2018, 〈10.1109/TMECH.2018.2836934〉. 〈hal-01792494〉

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