Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9613
• Main Authors: Vásconez, Juan Pablo, Barona López, Lorena Isabel, Valdivieso Caraguay, Ángel Leonardo, Benalcázar, Marco E
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.12.2022; MDPI
• Subjects: Algorithms; Analysis; Classification; Datasets; deep Q-network; Electromyography; Electromyography - methods; Gesture recognition; Gestures; Hand; hand gesture recognition; inertial measurement unit; Inertial platforms; Machine learning; Machine tools; Methods; Muscle function; Neural networks; Neural Networks, Computer; Online education; reinforcement learning; Sensors; Supervised learning; Technology application; Upper Extremity; inertial measurement unit; deep Q-network; electromyography; hand gesture recognition; reinforcement learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22249613

Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures-five static and six dynamic-using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to 97.50%±1.13% and 88.15%±2.84% for the classification and recognition, respectively, with regard to static gestures, and 98.95%±0.62% and 90.47%±4.57% for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals.