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首页> 外文期刊>Technologies >Tactile Myography: An Off-Line Assessment of Able-Bodied Subjects and One Upper-Limb Amputee
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Tactile Myography: An Off-Line Assessment of Able-Bodied Subjects and One Upper-Limb Amputee

机译:触觉肌电图:对身体状况良好的受试者和一名上肢截肢者的离线评估

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Human-machine interfaces to control prosthetic devices still suffer from scarce dexterity and low reliability; for this reason, the community of assistive robotics is exploring novel solutions to the problem of myocontrol. In this work, we present experimental results pointing in the direction that one such method, namely Tactile Myography (TMG), can improve the situation. In particular, we use a shape-conformable high-resolution tactile bracelet wrapped around the forearm/residual limb to discriminate several wrist and finger activations performed by able-bodied subjects and a trans-radial amputee. Several combinations of features/classifiers were tested to discriminate among the activations. The balanced accuracy obtained by the best classifier/feature combination was on average 89.15% (able-bodied subjects) and 88.72% (amputated subject); when considering wrist activations only, the results were on average 98.44% for the able-bodied subjects and 98.72% for the amputee. The results obtained from the amputee were comparable to those obtained by the able-bodied subjects. This suggests that TMG is a viable technique for myoprosthetic control, either as a replacement of or as a companion to traditional surface electromyography.
机译:用于控制假肢设备的人机界面仍然缺乏灵巧性和低可靠性。由于这个原因,辅助机器人社区正在探索解决肌控制问题的新方法。在这项工作中,我们提出了实验结果,指出了一种这样的方法,即触觉肌成像(TMG)可以改善这种情况的方向。尤其是,我们使用缠绕在前臂/残肢周围的形状合适的高分辨率触觉手链,以区分身体健全的受试者和经-骨截肢者进行的几次手腕和手指激活。测试了特征/分类器的几种组合以区分激活。最佳分类器/功能组合所获得的平衡准确度平均为89.15%(健全受试者)和88.72%(截肢受试者);仅考虑手腕激活时,身体健全的受试者的平均结果为98.44%,截肢者的平均结果为98.72%。从截肢者获得的结果与健全受试者的结果相当。这表明,TMG是一种用于肌假体控制的可行技术,可替代传统表面肌电图或作为其替代品。

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