Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface electromyography (sEMG) has been explored for its rich informational context and accessibility when combined with advanced machine learning approaches and wearable systems. The literature presents numerous approaches to boost performance while ensuring robustness for neurorobots using sEMG, often resulting in models requiring high processing power, large datasets, and less scalable solutions. This paper addresses this challenge by proposing the decoding of muscle synchronization rather than individual muscle activation. We study coherence-based functional muscle networks as the core of our perception model, proposing that functional synchronization between muscles and the graph-based network of muscle connectivity encode contextual information about intended hand gestures. This can be decoded using shallow machine learning approaches without the need for deep temporal networks. Our technique could impact myoelectric control of neurorobots by reducing computational burdens and enhancing efficiency. The approach is benchmarked on the Ninapro database, which contains 12 EMG signals from 40 subjects performing 17 hand gestures. It achieves an accuracy of 85.1%, demonstrating improved performance compared to existing methods while requiring much less computational power. The results support the hypothesis that a coherence-based functional muscle network encodes critical information related to gesture execution, significantly enhancing hand gesture perception with potential applications for neurorobotic systems and interactive machines.
EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
National Research Center for Optical Instrumentation, Zhejiang University（浙江大学光学仪器国家研究中心）；School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学人工智能与机器人学院和机器人视觉感知与控制技术国家工程研究中心）；Ant Group Company Ltd.（蚂蚁集团有限公司）
Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
Dalian University of Technology（大连理工大学）；The University of Tokyo（东京大学）；Dalian Medical University（大连医科大学）；Second Affiliated Hospital of Dalian Medical University（大连医科大学附属第二医院）
A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
Real-time Human Finger Pointing Recognition and Estimation for Robot Directives Using a Single Web-Camera
Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT)（麻省理工学院机械工程系）；School of Mechanical Engineering, Tel-Aviv University（特拉维夫大学机械工程学院）
Bidirectional Intent Communication: A Role for Large Foundation Models
TU Chemnitz（开姆尼茨工业大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；Technical University of Munich（慕尼黑工业大学）；Centre for Applied Autonomous Sensor Systems (AASS), Örebro University（厄勒布鲁大学应用自主传感系统中心）
