Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
Authors: Jiaxuan Li, Di Wu, Jianhua Liu, Yuxin Zhao, Jinnuo Li, Xiao Zhang, Zhenzhi Ying, Changsheng Dai, +2 more
Organizations: Intelligent Equipment and Medical Device Laboratory, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China. · Manufacturing Laboratory, Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8656, Japan. · Institute of Robotics and Intelligent Systems, Dalian University of Technology, Dalian 116024, China. · Second Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian 116024, China
Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter benchmark for dexterous decoding. The underlying data-algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.