{"type":"ScholarlyArticle","title":"BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation","authors":[{"name":"Nana Wang","slug":"nana-wang","given":"Nana","family":"Wang","affiliation":"State Key Lab of VR Technology and System , School of CSE , Beihang University , Beijing , China; NoBarriers.ai Technology , Hangzhou , China"},{"name":"Jianwei Niu","slug":"jianwei-niu","given":"Jianwei","family":"Niu","affiliation":"State Key Lab of VR Technology and System , School of CSE , Beihang University , Beijing , China; School of Computer and Artificial Intelligence , Zhengzhou University , Zhengzhou , China"},{"name":"Xuefeng Liu","slug":"xuefeng-liu","given":"Xuefeng","family":"Liu","affiliation":"State Key Lab of VR Technology and System , School of CSE , Beihang University , Beijing , China"},{"name":"Dongqin Yu","slug":"dongqin-yu","given":"Dongqin","family":"Yu","affiliation":"NoBarriers.ai Technology , Hangzhou , China"},{"name":"Guogang Zhu","slug":"guogang-zhu","given":"Guogang","family":"Zhu","affiliation":"State Key Lab of VR Technology and System , School of CSE , Beihang University , Beijing , China"},{"name":"Xinghao Wu","slug":"xinghao-wu","given":"Xinghao","family":"Wu","affiliation":"State Key Lab of VR Technology and System , School of CSE , Beihang University , Beijing , China"},{"name":"Mingliang Xu","slug":"mingliang-xu","given":"Mingliang","family":"Xu","affiliation":"School of Computer and Artificial Intelligence , Zhengzhou University , Zhengzhou , China"},{"name":"Hao Su","slug":"hao-su","given":"Hao","family":"Su","affiliation":"School of Computer and Artificial Intelligence , Zhengzhou University , Zhengzhou , China"}],"abstract":"Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e.g., pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. BeyondVision consists of a wristband-style EMG sampling device and a tailored lightweight neural network BV-Net that can accurately translate EMG signals of MHGs to control commands in real-time. Moreover, we propose a post-processing mechanism and a weight segmentation algorithm to effectively improve the accuracy rate of MHG recognition. Subjective and objective experimental results show that our approach achieves over 95% average recognition rate, 2000Hz sampling frequency, and real-time micro gesture recognition. Our technique has been applied in a commercially available product, introduced at: https://github.com/tyc333/NoBarriers.","doi":"10.24963/ijcai.2024/668","url":"https://ijcai.org/proceedings/2024/668","pdf":"https://ijcai.org/proceedings/2024/0668.pdf","markdown":"https://ijcai.org/proceedings/2024/668.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/668","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":668,"pages":{"first":6044,"last":6052},"keywords":["Computer Vision: CV: Biometrics, face, gesture and pose recognition","Multidisciplinary Topics and Applications: MTA: AI hardware","Multidisciplinary Topics and Applications: MTA: Interactive entertainment","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Atzori et al., 2014] Manfredo Atzori, Arjan Gijsberts, Castellini, and et al. 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