Robust neural decoding for dexterous control of robotic hand kinematics
Robust neural decoding for dexterous control of robotic hand kinematics
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DOI：
10.1016/j.compbiomed.2023.107139
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发表时间：
2023-06
影响因子：
7.7
通讯作者：
Jiahao Fan;Luis Vargas;Derek G. Kamper;Xiaogang Hu
中科院分区：
文献类型：
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作者：
Jiahao Fan;Luis Vargas;Derek G. Kamper;Xiaogang Hu
背景手动灵巧是一种基本的运动技能，它使我们能够完成复杂的日常任务。然而，神经肌肉损伤可能会导致手部灵活性的丧失。虽然已经开发出了许多先进的辅助机械手，但我们仍然缺乏对多自由度的灵活和连续的实时控制。在这项研究中，我们开发了一种高效和健壮的神经解码方法，可以连续解码预期的手指动态运动，以实时控制假手。方法在参与者进行单指或多指屈伸动作时，从外在的手指屈肌和伸肌获得高密度肌电(HD-EMG)信号。我们实现了一种基于深度学习的神经网络方法来学习从HD-EMG特征到手指特定群体运动神经元放电频率(即神经驱动信号)的映射。神经驱动信号反映了特定于单个手指的运动指令。结果与直接基于指力信号训练的深度学习模型和传统的肌电幅值估计方法相比，所开发的神经驱动解码器在单指和多指任务中能够一致、准确地预测关节角度，并且预测误差显著降低。解码器的性能随着时间的推移是稳定的，并且对肌电信号的变化具有健壮性。结论该神经解码技术提供了一种新颖高效的神经-机器接口，能够高精度地一致预测机器人手指的运动学，从而实现对辅助机械手的灵巧控制。
BackgroundManual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand.MethodsHigh-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time.ResultsOur developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers.ConclusionsThis neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.