A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning

• URL: http://arxiv.org/abs/2310.03752v1
• Date: Sat, 23 Sep 2023 05:32:33 GMT
• Title: A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
• Authors: Golara Ahmadi Azar, Qin Hu, Melika Emami, Alyson Fletcher, Sundeep Rangan, S. Farokh Atashzar
• Abstract summary: Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computers. These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons. These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons.
• Score: 11.170031300110315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG). These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons. However, the natural variability of sEMG among individuals has led researchers to focus on subject-specific solutions. Deep learning methods, which often have complex structures, are particularly data-hungry and can be time-consuming to train, making them less practical for subject-specific applications. In this paper, we propose and develop a generalizable, sequential decoder of transient high-density sEMG (HD-sEMG) that achieves 73% average accuracy on 65 gestures for partially-observed subjects through subject-embedded transfer learning, leveraging pre-knowledge of HGR acquired during pre-training. The use of transient HD-sEMG before gesture stabilization allows us to predict gestures with the ultimate goal of counterbalancing system control delays. The results show that the proposed generalized models significantly outperform subject-specific approaches, especially when the training data is limited, and there is a significant number of gesture classes. By building on pre-knowledge and incorporating a multiplicative subject-embedded structure, our method comparatively achieves more than 13% average accuracy across partially observed subjects with minimal data availability. This work highlights the potential of HD-sEMG and demonstrates the benefits of modeling common patterns across users to reduce the need for large amounts of data for new users, enhancing practicality.

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