Contact Detection and Location from Robot and Object Tracking on RGB-D Images

Electromyographic (EMG) signal is the electrical manifestation of a muscle contraction. Surface EMG signal can be obtained by electrodes on the skin to control prosthetic hand. However, surface EMG is sensitive to environmental interference, which leads to a low motion recognition rate of prosthesis control when encountering unexpected interferences, like electrodes shift. Electrodes shift occurs particularly in the day-today use of wearing electrodes. As a reslut, a long-term training procedure is necessary. To solve this problem, this paper proposes a new sEMG electrodes configuration to reduce the interference caused by electrodes shift. Experiments are designed to verify the improvements through evaluating the classification accuracy of discriminating eleven hand motions by pattern recognition approach. The comparison results show that the proposed electrodes configuration increases the pattern recognition rate by 4% and 8% when applied kNN and LDA classifier, respectively. This paper suggests that optimising electrodes configuration is able to improve the EMG pattern discrimination and the proposed electrodes configuration has reference value.

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Towards Hand-Object Gesture Extraction from Depth Image

Gao

Cao

et al. 2016

2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Ad

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Contact Detection and Location from Robot and Object Tracking on RGB-D Images

Cited by 1 publication

References 13 publications

Towards Hand-Object Gesture Extraction from Depth Image

Towards Hand-Object Gesture Extraction from Depth Image

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