A novel framework for designing a multi-DoF prosthetic wrist control using machine learning.
A novel framework for designing a multi-DoF prosthetic wrist control using machine learning.
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DOI：
10.1038/s41598-021-94449-1
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发表时间：
2021-07-22
影响因子：
4.6
通讯作者：
Kang J
中科院分区：
文献类型：
--
作者：
Swami CP;Lenhard N;Kang J
义肢可以显著提高上肢丧失患者的上肢功能，但尽管各种多自由度义肢已经发展出来，但义肢遗弃率仍然很高。其中一个主要的挑战是设计一个多自由度的控制器，具有高精度，鲁棒性和直观的日常使用。本研究展示了一种新的框架，用于开发利用机器学习算法和运动协同作用的控制器，以实现用于日常生活活动（ADL）的二自由度假肢手腕的自然控制。数据是在十个人的ADL任务中收集的，他们戴着腕带来模拟腕部功能的缺失。利用这些数据，神经网络对运动进行分类，然后随机森林回归计算出假肢手腕的期望速度。使用adl对模型进行训练/测试，其中使用交叉验证和保留数据集测试其稳健性。所提出的框架显示出很高的准确性（分类器的F-1分数为99%，回归的Pearson相关系数为0.98）。此外，随机森林回归的可解释性被用来验证目标运动协同效应。本工作为开发多自由度假肢装置的直观控制提供了一个新颖有效的框架。
Prosthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.
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作者：
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DOI：
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