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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand
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A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand

机译：基于EMG模式识别的假肢手的低成本实时研究平台

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摘要

The focus of this paper is the development of a low-cost research platform for a surface electromyogram (EMG)-based prosthetic hand control to evaluate various pattern recognition techniques and to study the real-time implementation. This comprehensible research platform may enlighten the biomedical research in developing countries for analyzing and evaluating the surface EMG signals. Major challenges in this work were as follows: the design and development of an EMG signal conditioning module, a pattern recognition module, and a prosthetic hand at low cost. Besides, EMG pattern recognition techniques were evaluated for identifying six hand motions in offline with signals acquired from ten healthy subjects and two transradial amputees. Features calculated from EMG signals were grouped into six ensembles to apprehend the vitality of the ensemble in classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree, logistic model tree, neural network, linear discriminant analysis, and support vector machine. The classification performance was also evaluated with the prolonged EMG data recorded on a day at every 1 h interval to study the robustness of the classifier. The results show the average classification accuracy, processing time and memory requirement of the SLR was found to be better and robust with time-domain features consisting of statistical as well as autoregression coefficients. The statistical analysis of variance test also showed that computation time and memory space required for SLR were significantly less compared to the other classifiers. The performance of the classifier was tested in online with transradial amputee for actuation of prosthetic hand for two intended motions with a TMS320F28335 controller. This proposed research platform for evaluation of EMG pattern recognition and real-time implementation has been achieved at a cost of 25 000 Indian rupee (INR).
机译：本文的重点是为基于表面肌电图（EMG）的假肢手控制开发低成本研究平台，以评估各种模式识别技术并研究实时实现。这个可理解的研究平台可能会启发发展中国家进行生物医学研究，以分析和评估表面肌电信号。这项工作的主要挑战如下：EMG信号调节模块，模式识别模块的设计和开发，以及低成本的假肢。此外，评估了肌电图模式识别技术，以从十个健康受试者和两个经trans动脉截肢者那里获得的信号离线识别六种手部动作。根据EMG信号计算得出的特征分为六个集合，以了解分类器中集合的生命力，即简单逻辑回归（SLR），决策树的J48算法，逻辑模型树，神经网络，线性判别分析和支持向量机。还使用每1小时间隔一天记录的长时间EMG数据评估了分类性能，以研究分类器的鲁棒性。结果表明，SLR的平均分类准确度，处理时间和内存需求更好，并且具有时域特征，包括统计系数和自回归系数，因此更加健壮。方差检验的统计分析还显示，与其他分类器相比，SLR所需的计算时间和内存空间显着减少。该分类器的性能已通过trans骨截肢者在线测试，以通过TMS320F28335控制器对假肢手进行两种预期动作。该拟议的用于评估EMG模式识别和实时实施的研究平台的成本为25,000印度卢比（INR），现已实现。

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