Human–Machine Interfaces Based on Bioelectric Signals: A Narrative Review with a Novel System Proposal

Hayashi, H.; Tsuji, Takao

doi:10.1002/tee.23646

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…We employed nonnegative matrix factorization (NNMF) algorithm [42] to extract the weighting matrix and the associated activation signals from the sEMG (or force) data matrix according to (1). NNMF is characterized by the nonnegativity of derived solutions, and thereby allowing correct physiological interpretation of the extracted synergies.…”

Section: Synergy Extractionmentioning

confidence: 99%

“…As a result, it usually requires a large amount of data and time for these nonlinear models to learn effectively, which may not be a good choice in real-world scenarios where the amputees prefer to control the prosthetic hand with less training time. In addition, synergies have long been regarded as the building blocks of complex motor behaviours, and the concept of muscle/force synergies are essentially based on a linear model given by (1). Over the last few decades, it has been well demonstrated that the linear model of synergies can be implemented mechanically using differential mechanisms to simplify the design and control of robotic hands [33][34][35].…”

Section: Correspondence Between Muscle and Force Synergiesmentioning

confidence: 99%

“…Surface electromyography (sEMG) have been widely implemented in human-machine interface to allow users to communicate with assistive devices, i.e. exoskeletons, prostheses, and orthoses [1][2][3][4]. This is especially the case for myocontrolled hand prostheses or exoskeletons due to the indispensability of human hand in activities of daily living [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Teng,

Xu,

Zhang

et al. 2023

J. Neural Eng.

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Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control. Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure. Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R 2 = 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R 2 = 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability. Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

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Section: Synergy Extractionmentioning

confidence: 99%

Section: Correspondence Between Muscle and Force Synergiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Teng,

Xu,

Zhang

et al. 2023

J. Neural Eng.

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“…The noise typically arises when there is movement in the body while collecting the EEG signal. Eliminating this noise from the signal can be a challenging task because the amplitude of the EEG signal is very low [Hayashi & Tsuji, 2022]. The headsets contain electrodes that capture signals from the brain.…”

Section: Pre-processingmentioning

confidence: 99%

Mental Stress Classification from Brain Signals using MLP Classifier

Samarpita,

Satpathy,

Kumar Mishra

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The most common and widespread mental condition that unavoidably affects people's mood and conduct is stress. The physiological reaction to powerful emotional, intellectual, and physical obstacles might be viewed as stress. As a result, early stress detection can result in solutions for potential improvements and ultimate event suppression. OBJECTIVES: To classify mental stress from the EEG signals of humans using an MLP classifier. METHODS: We examine the EEG signal analysis techniques currently in use for detecting mental stress using Multi-layer Perceptron (MLP). RESULTS: The suggested technique has a 95% classification accuracy performance. CONCLUSION: In our study, the use of MLP classifiers for stress detection from EEG signals has shown promising results. The high accuracy and precision of the classifiers, as well as the informative nature of certain EEG frequency bands, suggest that this approach could be a valuable tool for stress detection and management.

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“…EMG is a system that detects the muscles’ electric activity and its nerves’ biosignals [ 52 ]. The detected signals can give an indication of the muscles’ health [ 53 ]. However, in the case of speech recognition, the muscles’ movement can indicate the speech behaviour and its relationship with the tongue muscle motion [ 54 ].…”

Section: Introductionmentioning

confidence: 99%

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

et al. 2022

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Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.

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Human–Machine Interfaces Based on Bioelectric Signals: A Narrative Review with a Novel System Proposal

Cited by 8 publications

References 43 publications

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Mental Stress Classification from Brain Signals using MLP Classifier

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

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