Concurrent Prediction of Dexterous Finger Flexion and Extension Force via Deep Forest
Concurrent Prediction of Dexterous Finger Flexion and Extension Force via Deep Forest
复制标题
通过深度森林同时预测灵巧手指的屈伸力
DOI：
10.1109/embc40787.2023.10340256
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hu, Xiaogang
中科院分区：
文献类型：
--
作者：
Fan, Jiahao;Hu, Xiaogang
神经肌肉损伤会损害手的功能，并严重影响生活质量。这推动了先进的辅助机械手的发展。然而，目前的神经译码系统在提供对这些机器手的灵活控制方面的能力是有限的。在这项研究中，我们提出了一种利用高密度肌电(HD-EMG)信号同时预测三个手指的伸展和屈曲力的新方法。我们的方法使用两个深森林模型，即屈肌解码器和伸肌解码器，从肌电幅度特征中提取相关的表示。两个解码器的输出通过线性回归进行积分，以预测三个手指的力。对三个受试者的数据进行了评估，结果表明，在目标和非目标手指的预测误差和鲁棒性方面，该方法始终优于传统的基于肌电幅度的方法。这项工作提出了一种很有前途的神经解码方法，用于直观和灵活地控制辅助机器人手的指尖力。
Neuromuscular injuries can impair hand function and profoundly impacting the quality of life. This has motivated the development of advanced assistive robotic hands. However, the current neural decoder systems are limited in their ability to provide dexterous control of these robotic hands. In this study, we propose a novel method for predicting the extension and flexion force of three individual fingers concurrently using high-density electromyogram (HD-EMG) signals. Our method employs two deep forest models, the flexor decoder and the extensor decoder, to extract relevant representations from the EMG amplitude features. The outputs of the two decoders are integrated through linear regression to predict the forces of the three fingers. The proposed method was evaluated on data from three subjects and the results showed that it consistently outperforms the conventional EMG amplitude-based approach in terms of prediction error and robustness across both target and non-target fingers. This work presents a promising neural decoding approach for intuitive and dexterous control of the fingertip forces of assistive robotic hands.