A Recurrent Neural Network Provides Stable Across-Day Prosthetic Control for a Human Amputee with Implanted Intramuscular Electromyographic Recording Leads.
A Recurrent Neural Network Provides Stable Across-Day Prosthetic Control for a Human Amputee with Implanted Intramuscular Electromyographic Recording Leads.
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循环神经网络为植入肌内肌电记录导线的人类截肢者提供稳定的全天假肢控制。
DOI：
10.1109/embc46164.2021.9629580
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
George,JacobA
中科院分区：
文献类型：
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作者：
Thomson,CalebJ;Clark,GregoryA;George,JacobA
上肢假肢的控制通常具有挑战性和非直觉性，导致高达50%的假肢使用者放弃他们的假肢。卷积神经网络(CNN)和递归长短期记忆(LSTM)网络在从肌电信号中提取高自由度的运动意图方面显示出了良好的前景，从而提供了更直观和灵活的假肢控制。这些算法的下一个重要考虑是性能是否在多天内保持稳定。在这里，我们介绍了一种新的LSTM网络，并将其性能与以前建立的最先进的算法-CNN和改进的卡尔曼滤波(MKF)-在离线分析中进行比较，该分析使用了一名截肢者在425天内收集的76天肌肉内记录。具体地说，我们通过对第一天(1天、5天、10天、30天或60天)的数据进行训练，然后对最后16天的肌电信号进行测试，评估了每种算法随着时间的推移的健壮性。结果表明，在前几天的额外数据集上进行训练通常会降低所有算法的有意和无意运动的均方根误差(RMSE)。在用60天的数据训练的所有算法中，使用LSTM实现了最低的非预期动作的RMSE。与其他算法相比，LSTM还显示出意外移动的RMSE的跨日差异较小。总之，这项工作表明，这里介绍的LSTM算法可以为假肢用户提供更直观和灵活的控制，并且对多天数据的训练提高了随后几天的整体性能，至少对于离线分析是这样。
Upper-limb prosthetic control is often challenging and non-intuitive, leading to up to 50% of prostheses users abandoning their prostheses. Convolutional neural networks (CNN) and recurrent long short-term memory (LSTM) networks have shown promise in extracting high-degree-of-freedom motor intent from myoelectric signals, thereby providing more intuitive and dexterous prosthetic control. An important next consideration for these algorithms is if performance remains stable over multiple days. Here we introduce a new LSTM network and compare its performance to previously established state-of-the-art algorithms–a CNN and a modified Kalman filter (MKF)–in offline analyses using 76 days of intramuscular recordings from one amputee participant collected over 425 calendar days. Specifically, we assessed the robustness of each algorithm over time by training on data from the first (one, five, ten, 30, or 60) days and then testing on myoelectric signals on the last 16 days. Results indicate that training on additional datasets from prior days generally decreases the Root Mean Squared Error (RMSE) of intended and unintended movements for all algorithms. Across all algorithms trained with 60 days of data, the lowest RMSE for unintended movements was achieved with the LSTM. The LSTM also showed less across-day variance in RMSE of unintended movements relative to the other algorithms. Altogether this work suggests that the LSTM algorithm introduced here can provide more intuitive and dexterous control for prosthetic users, and that training on multiple days of data improves overall performance on subsequent days, at least for offline analyses.