期刊
ADVANCED ROBOTICS
卷 33, 期 5, 页码 254-263出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01691864.2018.1563500
关键词
Locomotion mode recognition; strain gauge; convolutional neural network; robotic transtibial prosthesis
类别
资金
- National Natural Science Foundation of China [91648207]
- National Key R&D Program of China [2018YFF0300606]
- Beijing Municipal Science and Technology Project [Z181100009218007]
- Natural Science Foundation of Beijing Municipality [18L2040]
- National Program for Support of Top-notch Young Professionals
Locomotion mode recognition can contribute to precise control of active lower-limb prostheses in different environments. In this paper, we propose a novel locomotion mode recognition method based on convolutional neural network and strain gauge signals. The strain gauge only provides one-dimensional signals and is also used in the control strategy of the robotic prosthesis. The convolutional neural network takes the raw noisy signals as inputs. Three transtibial amputee subjects were recruited in the experiments, and three locomotion modes were recognized. The overall three-class locomotion mode recognition accuracy is in the hold-out test and in the 5-fold cross-validation. The results show that the strain gauge contains information of locomotion modes, and the convolutional neural network has the capacity of extracting features from raw signals.
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