Hand motion recognition via fuzzy active curve axis Gaussian mixture models: A comparative study

Ju, Zhaojie; Liu, Honghai

doi:10.1109/fuzzy.2011.6007367

Cited by 3 publications

(1 citation statement)

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“…T HE surface EMG signal is widely used to recognise human motions [1], [2], [3], [4] and estimate muscular force [5]. Plenty of achievements have been made in their respective fields.…”

Section: Introductionmentioning

confidence: 99%

Finger pinch force estimation through muscle activations using a surface EMG sleeve on the forearm

Fang

Zhu

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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For prosthetic hand manipulation, the surface Electromyography(sEMG) has been widely applied. Researchers usually focus on the recognition of hand grasps or gestures, but ignore the hand force, which is equally important for robotic hand control. Therefore, this paper concentrates on the methods of finger forces estimation based on multichannel sEMG signal. A custom-made sEMG sleeve system omitting the stage of muscle positioning is utilised to capture the sEMG signal on the forearm. A mathematic model for muscle activation extraction is established to describe the relationship between finger pinch forces and sEMG signal, where the genetic algorithm is employed to optimise the coefficients. The results of experiments in this paper shows three main contributions: 1) There is a systematical relationship between muscle activations and the pinch finger forces. 2) To estimate the finger force, muscle precise positioning for electrodes placement is not inevitable. 3) In a multi-channel EMG system, selecting specific combinations of several channels can improve the estimation accuracy for specific gestures.

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“…T HE surface EMG signal is widely used to recognise human motions [1], [2], [3], [4] and estimate muscular force [5]. Plenty of achievements have been made in their respective fields.…”

Section: Introductionmentioning

confidence: 99%