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Open AccessArticle

Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor

1
LIRIS, CNRS UMR 5205, École Centrale de Lyon, 69130 Ecully, France
2
SAMOVAR, CNRS UMR 5157, Telecom SudParis, Institut Polytechnique de Paris, 91011 Evry CEDEX, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4242; https://doi.org/10.3390/s19194242
Received: 16 July 2019 / Revised: 22 August 2019 / Accepted: 26 September 2019 / Published: 29 September 2019
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithms. View Full-Text
Keywords: gait analysis; lower limb locomotion activity; triplet Markov model; semi-Markov model; on-line EM algorithm gait analysis; lower limb locomotion activity; triplet Markov model; semi-Markov model; on-line EM algorithm
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Li, H.; Derrode, S.; Pieczynski, W. Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor. Sensors 2019, 19, 4242.

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