Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Sensors Année : 2019

Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor

Résumé

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
Fichier principal
Vignette du fichier
sensors-19-04242.pdf (2.7 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-02351365 , version 1 (08-11-2019)

Identifiants

Citer

Haoyu Li, Stéphane Derrode, Wojciech Pieczynski. Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor. Sensors, 2019, Sensors for Gait, Posture, and Health Monitoring, 19, pp.4242-1. ⟨10.3390/s19194242⟩. ⟨hal-02351365⟩
134 Consultations
87 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More