Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG

Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its...

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Bibliographic Details
Published in:Biosensors (Basel) 2022-01, Vol.12 (2), p.57
Main Authors: Wang, Shuo, Zheng, Jingjing, Zheng, Bin, Jiang, Xianta
Format: Article
Language:English
Subjects:
Accuracy
Amputation
Artificial Limbs
Classification
Data collection
Electromyography
Electromyography - methods
Experiments
grasp classification
grasp phases analysis
Grasping
Hand
Hand Strength
Machine learning
myoelectric prosthesis
Pattern recognition
Prostheses
sEMG
Sensors
Standard deviation
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Summary:Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.
ISSN:2079-6374
2079-6374
DOI:10.3390/bios12020057