EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models

Furui, Akira; Igaue, Takuya; Tsuji, Toshio

doi:10.1016/j.eswa.2021.115644

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…The method normally involves stronger assumptions about the data generation process, such as the distribution of inputs and outputs. Importantly, it has application potential for building pattern recognition classifiers capable of decoding limb movement intentions and controlling myoelectric prostheses [50].…”

Section: Myoelectrical Control Of Bionic Handmentioning

confidence: 99%

Biorealistic hand prosthesis with compliance control and noninvasive somatotopic sensory feedback

Lan

Zhang

et al. 2023

Prog. Biomed. Eng.

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Significant advances have been made to improve control and to provide sensory functions for bionic hands. However, great challenges remain limiting wide acceptance of bionic hands due to inadequate bi-directional neural compatibility with human users. Recent research has brought to light the necessity for matching neuromechanical behaviors between the prosthesis and the sensorimotor system of amputees. A novel approach to achieving greater neural compatibility leverages the technology of biorealistic modeling with real-time computation. These studies have demonstrated a promising outlook that this unique approach may transform the performance of hand prostheses. Simultaneously, a non-invasive technique of somatotopic sensory feedback has been developed based on evoked tactile sensation (ETS) for conveying natural, intuitive and digit-specific tactile information to users. This article reports the recent work in these two important aspects of sensorimotor functions in prosthetic research. A background review is presented first on the state-of-the-art of bionic hand and the various techniques to deliver tactile sensory information to users. Then the progress in developing the novel biorealistic hand prosthesis and the technique of non-invasive ETS feedback is highlighted. Finally, challenges to future development of the biorealistic hand prosthesis and implementing the ETS feedback are discussed with respect to shaping a next-generation hand prosthesis.

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Section: Myoelectrical Control Of Bionic Handmentioning

confidence: 99%

Biorealistic hand prosthesis with compliance control and noninvasive somatotopic sensory feedback

Lan

Zhang

et al. 2023

Prog. Biomed. Eng.

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“…EMG is used to diagnose neurological and neuromuscular problems, and many neurorehabilitation systems integrate this technology thanks to its motion determination capability, using EMG pattern recognition methods. [15]…”

Section: Electromyography (Emg) Feedbackmentioning

confidence: 99%

A Review Regarding Neurorehabilitation Technologies for Hand Motor Functions

Hamos¹,

Ţarcă²,

Birouas³

et al. 2022

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The paper deals with a short review regarding neurorehabilitation technologies for regaining human hand mobility functions after a cerebrovascular accident or stroke. The aim of this paper is to form a general understanding of the current technologies used in the field of neurorehabilitation and highlight key characteristics, advantages and disadvantages. Technologies that are studies include robot exoskeletons, electro stimulation, brain computer interfaces (BCI), EEG and limb mounted sensors. After a presenting a summary of current existing technologies, a brief conclusion proposing the future direction of this study is proposed.

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On the detection of activity patterns in electromyographic signals via decision trees

Ramírez-Pérez

Guerrero-Díaz-de-León

Macías‐Díaz

2023

Evol. Intel.

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EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models

Cited by 10 publications

References 39 publications

Biorealistic hand prosthesis with compliance control and noninvasive somatotopic sensory feedback

Biorealistic hand prosthesis with compliance control and noninvasive somatotopic sensory feedback

A Review Regarding Neurorehabilitation Technologies for Hand Motor Functions

On the detection of activity patterns in electromyographic signals via decision trees

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