The use of surface electromyography (EMG) and Inertial Measurement Unit (IMU) data emerged as a possible alternative to computer vision-based gesture recognition. As a consequence, the convenience of using such data in the automatic recognition of sign languages, a natural application of gesture recognition, has been investigated in scientific literature. Most of the methodologies and evaluations are based on traditional machine learning techniques, such as SVMs, relying on selected handcrafted features. Instead, leveraging on the findings about deep Long Short Term Memory (LSTM) architectures to process time series, we propose a deep LSTM-based neural network for the recognition of the Italian Sign Language alphabet with surface EMG and IMU data. To preliminary validate our methodology, we collected a dataset recording gesture samples with the Myo Gesture Control Armband. We obtained a 97% accuracy on the proposed dataset.

Italian sign language alphabet recognition from surface EMG and IMU sensors with a deep neural network

Sernani P.
;
Falcionelli N.;Tomassini S.;Dragoni A. F.
2021

Abstract

The use of surface electromyography (EMG) and Inertial Measurement Unit (IMU) data emerged as a possible alternative to computer vision-based gesture recognition. As a consequence, the convenience of using such data in the automatic recognition of sign languages, a natural application of gesture recognition, has been investigated in scientific literature. Most of the methodologies and evaluations are based on traditional machine learning techniques, such as SVMs, relying on selected handcrafted features. Instead, leveraging on the findings about deep Long Short Term Memory (LSTM) architectures to process time series, we propose a deep LSTM-based neural network for the recognition of the Italian Sign Language alphabet with surface EMG and IMU data. To preliminary validate our methodology, we collected a dataset recording gesture samples with the Myo Gesture Control Armband. We obtained a 97% accuracy on the proposed dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/290832
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