Towards Efficient Neural Decoder for Dexterous Finger Force Predictions
Towards Efficient Neural Decoder for Dexterous Finger Force Predictions
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DOI：
10.1109/tbme.2024.3353145
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发表时间：
2024-06-01
影响因子：
4.6
通讯作者：
Hu，Xiaogang
中科院分区：
文献类型：
--
作者：
Fan，Jiahao;Hu，Xiaogang
目的：机器人手的灵巧控制需要一个鲁棒的神经-机器接口，能够准确解码多个手指运动。现有的解码器训练研究主要集中在单指运动，或严重依赖多指数据进行解码器训练，这需要庞大的数据集和高计算需求。在这项研究中，我们研究了使用有限的单指表面肌电图（sEMG）数据来训练神经解码器的可行性，该解码器能够预测看不见的多指组合的力量。方法我们开发了一个基于深度森林的神经解码器来同时预测三根手指（食指、中指和无名指）的伸屈力。我们在有限条件下（即单指数据）使用不同数量的高密度肌电图数据来训练模型。结果深度森林解码器的力预测误差为7.0%，预测值为0.874，显著优于传统的肌电振幅法和卷积神经网络解码器。然而，当用于训练的数据量较少以及测试数据有噪声时，深度森林解码器的精度会下降。结论深度森林解码器在多指力预测任务中表现准确。深度森林的效率在于训练时间短和训练数据量小，这是当前神经解码应用的两个关键因素。本研究为先进的机械手控制提供了高效、准确的神经解码器训练，具有在人机交互中实际应用的潜力。
ObjectiveDexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations.MethodsWe developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data).ResultsWe showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors andvalue of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy.ConclusionThe deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications.SignificanceThis study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.