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

Haoyu Li 1 Stéphane Derrode 1 Wojciech Pieczynski 2
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
Abstract : Lower limb locomotion activity is of great interest in the field of human activity recognition. 1 In this work, a semi-Markov triplet model-based method is proposed to recognise the locomotion 2 activities when lower limbs move periodically. In the proposed algorithm, the gait phases (or 3 leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to 4 represent the complex conditioned observation density. The introduced sojourn state forms the 5 semi-Markov structure, which naturally replicates the real transition of activity and gait during 6 motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed 7 respectively for model training and adaptive on-line recognition. The algorithm is tested on two 8 datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up 9 to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time 10 required for model updating. Experimental results show an improvement of performance compared 11 to the other competitive algorithms. 12
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Haoyu Li, Stéphane Derrode, Wojciech Pieczynski. Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor. Sensors, MDPI, 2019, Sensors for Gait, Posture, and Health Monitoring, 19 (19), pp.4242. ⟨10.3390/s19194242⟩. ⟨hal-02351365⟩

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