基于躯干肌肉活动监测的手部行为识别
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TP931.4

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国家科技重大专项项目(2011ZX04002-111);2018年度闵行区重大产业技术攻关计划项目(2018MH208)


Research on Hand Behavior Recognition Based on Trunk Muscle Activity Monitoring
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    摘要:

    为解决某些手部工作意图不能由手部的运动和肌肉行为来反映的问题,拟通过监测部分躯干肌肉的协作方式,识别手部行为意图。因此在限定任务的情况下,设计了伴有单手操作的弯伸腰实验。同步采集全身运动信号和一组椎旁肌肌电信号。调整、选择肌电信号的两步聚类细分程度。作为双向长短时神经网络的输入信号,肌肉组行为标注步骤的F1值平均为91.37%。最终确认,从躯干肌肉群行为抽取的编码可作为信号源,识别手部精确控制、维持平衡等意图。

    Abstract:

    In order to solve the problem that certain work intention of the hands can’t be reflected by behaviors or myoelectric signals of the hands. The cooperation of some trunk muscles were monitored to identify the intention of the hands. Therefore, within limited tasks, an experiment including flexion-extension tasks accompanied by single hand operation was designed. The motion signals of the whole body and myoelectric signals of a group of paravertebral muscle were collected simultaneously. Adjust and select the two-step clustering subdivision degree of myoelectric signals. Have the clustering results as the input signal of bidirectional long short term neural network, the averaged F1 value of the muscle behavior labeling step was 91.37%. Finally, it is confirmed that the codes extracted from the trunk muscle group can be used as a signal source to identify the intentions including precise control of the hands and maintenance of balance.

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王琦,王庆明. 基于躯干肌肉活动监测的手部行为识别[J]. 科学技术与工程, 2021, 21(11): 4550-4555.
Wang Qi, Wang Qingming. Research on Hand Behavior Recognition Based on Trunk Muscle Activity Monitoring[J]. Science Technology and Engineering,2021,21(11):4550-4555.

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历史
  • 收稿日期:2020-06-01
  • 最后修改日期:2021-01-19
  • 录用日期:2020-12-01
  • 在线发布日期: 2021-05-17
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