Dynamic Manipulation Skill Learning for Tactile Myoelectric Prosthetic Hands in Tool Handling

Continuous tool operation with a myoelectric prosthetic hand is considerably more complex than discrete grasping tasks. This complexity arises because the control system must maintain stable, adaptive, and coordinated motions under varying loads and unpredictable interactions. In human motor control, this stability is achieved through a biological sensorimotor closed loop, where tactile feedback continuously modulates neural signals to adapt to environmental changes. Inspired by these mechanisms for reducing grasp instability caused by external shocks, this study designed a multimodal controller termed the tactile, kinesthetic, and electromyography (EMG) bionic gripping controller (TKE-BGC). It integrates tactile, kinematic, and EMG information. Initially, multimodal data—encompassing tactile signals, joint angles, and EMG patterns—were collected from able-bodied users during tool manipulation via a data glove. Subsequently, the TKE-BGC model was trained on these data, utilizing a Transformer encoder to extract high-level features and a multilayer perceptron to predict joint angles in real time. Based on this controller, this paper presents a prosthetic control framework developed through human skill transfer. Unlike conventional fixed force or force follows strategies that struggle with dynamic impacts or tracking delays, this framework enables robust end-to-end adaptive control. Tested across 4 seen and unseen tool operation tasks, the proposed method demonstrated precise detailed performance. Specifically, it significantly reduced the number of tool drops and shortened task completion times compared to the baseline methods. Furthermore, it achieved human-like average contact forces and substantially lowered the user’s physical workload, requiring noticeably less muscle effort than the force follows strategy (e.g., average EMG amplitude, 0.0023 versus 0.0124). By rapidly adjusting grip force through feedback and effectively mitigating instability, this research holds significant practical value in enhancing the daily independence of amputees and supporting their vocational rehabilitation and reemployment.