IEEE Trans Neural Syst Rehabil Eng. 2023;31:4821-4830. doi: 10.1109/TNSRE.2023.3336543. Epub 2023 Dec 8.
There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.
人们对利用剩余肌肉活动进行动力下肢假肢的神经控制越来越感兴趣。然而，仅研究了基于表面肌电图 (EMG) 的解码器。本研究旨在研究基于运动单位 (MU) 的解码方法作为替代基于 EMG 的意图识别的潜力，用于踝关节扭矩估计。8 名非截肢者 (NON) 和 7 名截肢者 (AMP) 参与了实验。受试者通过跟踪胫骨前肌 (TA) 和腓肠肌 (GA) 的期望肌肉活动，用完好的肢体进行等距背屈和跖屈，同时记录踝关节扭矩。为了匹配幻肢和完好肢体的活动，AMP 通过其残余 TA 和 GA 镜像肌肉激活。我们比较了基于 1) EMG 幅度 (aEMG)、2) 代表神经驱动 (ND) 的 MU 放电频率和 3) 与建模的抽搐力卷积的 MU 放电 (MUDrive) 的神经肌肉解码器 (线性回归) 进行踝关节扭矩估计。此外，对 MUDrive 方法进行了敏感性分析和优化降维，以进一步提高其实际价值。我们的结果表明，MUDrive 在 NON 的肌肉中以及 AMP 的完好和残余肌肉中，明显优于 EMG 和 ND 方法 (具有较低的均方根误差)。减少优化的 MUDrive 参数数量会降低性能。即便如此，优化计算时间减少了，MUDrive 仍然优于 aEMG。我们的结果表明，将 MU 放电与建模的生物力学输出相结合，可能比直接使用 EMG 控制辅助、下肢设备（如外骨骼和动力假肢）提供更准确的扭矩控制信号。
