基于肌电图模式识别的基于各种机器学习技术的假肢控制

Q3 Engineering
Sushil Ghildiyal, Geetha Mani, Ruban Nersisson
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引用次数: 1

摘要

由于截肢和神经系统疾病而失去肢体的人每天早上都要面对这种损失。根据文献综述,近30%的印度人患有上肢截肢。作为一项补救措施,一种力控假肢已经被开发出来,以提高他们的自立能力,生活质量和精神力量。目前的假肢手术是通过残肌收缩来完成的,残肌收缩有助于传感器和马达的激活。但也有一些缺点,截肢者不知道需要施加多大的压力来握住各种物体。此外,截肢者还必须接受外科手术。然而,本文提出了一种使用不同的机器学习(ML)回归方法来预测调节伺服电机电压所需的力的方法。支持向量回归(SVR)、线性回归和随机森林模型已被用于预测力需求。在对结果进行比较后,随机森林模型对控制直流伺服电机电压所需的力给出了高度准确的预测
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electromyography pattern-recognition based prosthetic limb control using various machine learning techniques
Abstract People who have lost their limbs to amputation and neurological disorders confront this loss every morning. As per the literature review, nearly 30% of the Indian population suffered from upper extremity amputation. As a coping-up measure, a force-controlled prosthetic limb has been developed to improve their self-reliance, quality of lifestyle and mental strength. The current prosthetic limb operation is done by residual muscle contraction, which contributes to the activation of the sensor and the motor. But there are some cons, the amputee does not know how much pressure needs to be exerted for holding various objects. Also, the amputee still has to undergo the surgical procedure. However, this paper proposes a way to predict the force which is needed to regulate the voltage for the servomotors using different Machine Learning (ML) regression approaches. Support Vector Regressor (SVR), Linear Regression and Random Forest models have been used to predict that force requirement. After comparing the results, the Random Forest model gave a highly accurate prediction of the force needed to control the voltage for the DC servomotors
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来源期刊
Journal of Medical Engineering and Technology
Journal of Medical Engineering and Technology Engineering-Biomedical Engineering
CiteScore
4.60
自引率
0.00%
发文量
77
期刊介绍: The Journal of Medical Engineering & Technology is an international, independent, multidisciplinary, bimonthly journal promoting an understanding of the physiological processes underlying disease processes and the appropriate application of technology. Features include authoritative review papers, the reporting of original research, and evaluation reports on new and existing techniques and devices. Each issue of the journal contains a comprehensive information service which provides news relevant to the world of medical technology, details of new products, book reviews, and selected contents of related journals.
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