The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

中文翻译：

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基于EMG的拾取和放置任务分类。

截肢者被剥夺了日常活动的次数。为了帮助截肢者，重要的是要了解肌肉活动的方式。任务有几个要素，涉及前臂以及手腕和手。一个非常重要的任务是用手进行拾取和放置活动。拾取和放置动作是不同手指动作的汇编，用于以不同的力度级别抓握对象。通过学习前臂肌肉的肌电信号可以更好地理解该动作。肌电图检查是一种用于获取肌肉活动的技术，该技术用于辅助设备的模式识别技术。关于这一点，涉及拾取和放置动作的EMG信号的不同分类特性，在这项研究中考虑了可变的抓地力跨度和重量。设计并开发了一种能够承受重量和物体跨度变化的低水平测力夹具，以模拟该任务。抓地力范围从6厘米到9厘米不等，本研究中使用的最大重量为750克。进行模式识别分类方法以区分抓取和放置活动，握力和掌指骨（MCP）关节的角度偏差的阶段。在这项研究中使用的分类器是基于EMG信号特征集的决策树（DT），支持向量机（SVM）和k最近邻（k-NN）。经过分析，结果发现，k-NN对MCP关节活动的不同阶段和相对偏差的分类效果最佳，其平均分类准确率分别为82％和91％。然而; SVM在具有特定功能集的力分类中表现最佳。这项研究的结果将有助于设计手截肢者的辅助设备。