Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition

Botros, Fady S.; Phinyomark, Angkoon; Scheme, Erik

doi:10.1109/access.2022.3225761

Cited by 14 publications

(7 citation statements)

References 48 publications

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“…Each detected activation burst was then segmented with 150 ms length windows overlapping of 75 ms each other, consistently with common practice in myoelectric pattern recognition problems [26], [27], [28]. Eventually, for each subject, a total of 3900 samples were obtained for training and testing machine learning models.…”

Section: Signal Pre-processingmentioning

confidence: 99%

Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

et al. 2023

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Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like the handwriting, is less studied. However, writing tasks involving cognitive loads represent an important aspect toward the generalization of myoelectric-based human-machine interfaces (HMI), and also for many rehabilitative tasks. In this study, the handwriting recognition of the ten digits was faced under the myoelectric control perspective, considering the probes setup and the feature extraction step. Time and frequency domain features were extracted from surface electromyography (sEMG) signals of 11 subjects who wrote the ten digits following a standardized template and 8 sEMG probes were equally distributed between forearm and wrist. Feature class separability was investigated and an aggregated feature set was built to train pattern recognition architectures, i.e. linear discriminant analysis (LDA) and quadratic support vector machine (QSVM). Also, four reduced probes setups were investigated. LDA and QSVM showed mean accuracy of about 97%, with all the forearm and wrist sEMG information. A significant reduction of performances was observed considering the wrist or the forearm only (≤92%) and when LDA and QSVM were trained with two electrodes information (≤90%). For the reliable classification performances in a motor task involving high cognitive demands, like the handwriting, it is required the use of probes fully covering forearm and wrist. Outcomes support the methodological transfer from myoelectric hand gesture to the handwriting recognition, which represents a key aspect in the development of new HMI for rehabilitation tasks.INDEX TERMS Handwriting, myoelectric pattern recognition, feature extraction, human-machine interface. I. INTRODUCTIONRecently, the use of surface electromyography (sEMG) for human-machine interfaces (HMI) is finding a widespread consensus in the literature [1], [2], [3], [4]. Indeed, myoelectric control proved to be valuable in different fields, i.e. from robotics to prosthesis, where the sEMG signals are used for decoding human intent in high-level control architectures [3], [5], [6]. This signal can be used to opportunely interactThe associate editor coordinating the review of this manuscript and approving it for publication was Xianzhi Wang .

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Section: Signal Pre-processingmentioning

confidence: 99%

Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

et al. 2023

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“…Performance of the proposed SD-DDA was tested on four state-of-the-art sEMG feature sets [5,[34][35][36][37][38] including normalized TD4 (TD4N), RMS and fifthorder AR coefficient (TDAR), time domain power spectral descriptors (TDPSD), and improved discrete Fourier transform (iDFT). Six advanced TL algorithms, including CCA, principal components analysis (PCA), geodesic flow Kernel (GFK), transfer component analysis (TCA), joint distribution adaptation (JDA) and DDA [17,21,22,26,27], were compared with the SD-DDA algorithm.…”

Section: Performance Evaluation and Validation 241 Performance Evalua...mentioning

confidence: 99%

“…Surface electromyography (sEMG) is commonly used to decode human physiological activities and monitor neuromuscular functions due to its advantages of simple acquisition and rich information [1,2]. Through the analysis of sEMG signals using pattern recognition techniques, the motion intent contained in the EMG signals can be decoded, serving as a control signal for prosthetic/rehabilitated hand [3,4] or an interactive signal in virtual and augmented reality scenarios [5,6]. In recent years, pattern recognition based on sEMG signals has become a research hotspot due to its ability to enable intuitive control of assistive devices and real-time interaction in human-computer systems.…”

Section: Introductionmentioning

confidence: 99%

A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition

Liu,

Peng,

Tan

et al. 2023

J. Neural Eng.

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Objective.Surface electromyography pattern recognition (sEMG-PR) is considered as a promising control method for human-machine interaction systems. However, the performance of a trained classifier would greatly degrade for novel users since sEMG signals are user-dependent and largely affected by a number of individual factors such as the quantity of subcutaneous fat and the skin impedance. Approach. To solve this issue, we proposed a novel unsupervised cross-individual motion recognition method that aligned sEMG features from different individuals by self-adaptive dimensional dynamic distribution adaptation (SD-DDA) in this study. In the method, both the distances of marginal and conditional distributions between source and target features were minimized through automatically selecting the optimal feature domain dimension by using a small amount of unlabelled target data.Main results.The effectiveness of the proposed method was tested on four different feature sets, and results showed that the average classification accuracy was improved by above 10% on our collected dataset with the best accuracy reached 90.4%. Compared to six kinds of classic transfer learning methods, the proposed method showed an outstanding performance with improvements of 3.2%-13.8%. Additionally, the proposed method achieved an approximate 9% improvement on a publicly available dataset. Significance. These results suggested that the proposed SD-DDA method is feasible for cross-individual motion intention recognition, which would provide help for the application of sEMG-PR based system.

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“…Generally, any factors that may hinder the realworld robustness of myoelectric control that are not accounted for during typical offline analyses-such as electrode shift [15], limb position effect [16], contraction intensity [17], and within/between day reliability [18]-are referred to as confounding factors [19]. While counteracting these confounding factors has been widely researched for continuous myoelectric control within the prosthetics community [15,20,21], there has been comparatively little consideration of their possible effects on general-purpose discrete myoelectric control.…”

Section: Introductionmentioning

confidence: 99%

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Eddy,

Campbell,

Bateman

et al. 2024

J. Neural Eng.

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Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to ``confounding factors'' (i.e., factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1) limb position variability, (2) cross-day use, and a newly identified confound faced by discrete systems (3) gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

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Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition

Cited by 14 publications

References 48 publications

Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

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