Communication Dans Un Congrès Année : 2019

Sensor fusion using EMG and vision for hand gesture classification in mobile applications

Résumé

The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation. This paper presents a mobile electromyography (EMG) analysis framework to be an auxiliary component in physiotherapy sessions or as a feedback for neuroprosthesis calibration. We implemented a framework that allows the integration of multisensors, EMG and visual information, to perform sensor fusion and to improve the accuracy of hand gesture recognition tasks. In particular, we used an event-based camera adapted to run on the limited computational resources of mobile phones. We introduced a new publicly available dataset of sensor fusion for hand gesture recognition recorded from 10 subjects and used it to train the recognition models offline. We compare the online results of the hand gesture recognition using the fusion approach with the individual sensors with an improvement in the accuracy of 13% and 11%, for EMG and vision respectively, reaching 85%.

Dates et versions

hal-02280202 , version 1 (06-09-2019)

Identifiants

Citer

Enea Ceolini, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati. Sensor fusion using EMG and vision for hand gesture classification in mobile applications. 2019 IEEE Biomedical Circuits and Systems Conference BIOCAS 2019, Oct 2019, Nara, Japan. 1-1 / Paper 8114, ⟨10.1109/BIOCAS.2019.8919163⟩. ⟨hal-02280202⟩
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