Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics

Pradhan, Ashirbad; He, Jiayuan; Jiang, Ning

doi:10.1038/s41597-022-01836-y

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…In recent years, there have been various initiatives to publish datasets of EMG signals and pose estimations (Atzori et al, 2014;Lobov et al, 2018;Jarque-Bou et al, 2019;Pradhan et al, 2022) aiming to provide the essential data required for improving machine learning models, myoelectric prostheses control, and, ultimately, restoring natural hand function for people with upperlimb disabilities. Typically, these datasets use either a clinical or consumer-grade EMG signals acquisition system with 8-12 EMG electrodes and a Cyberglove for hand motion capture.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Simar,

Colot,

Cebolla

et al. 2024

Front. Neurosci.

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Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the “move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

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Section: Discussionmentioning

confidence: 99%

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Simar,

Colot,

Cebolla

et al. 2024

Front. Neurosci.

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“…Recently, a study reported that wrist EMG signals have comparable performance with forearm EMG signals for hand gesture recognition [11]. Furthermore, [11] and other studies [12,13] showed that wrist EMG signals have comparable signal quality metrics with forearm signals at least. However, to the best of our knowledge, the difference in authentication performance between wrist and forearm EMG signals remains to be compared.…”

Section: Introductionmentioning

confidence: 93%

“…A previously collected EMG dataset by us, named Gesture Recognition and Biometrics electroMyogram (GRABMyo) Dataset, was used in this study. The data acquisition experiment protocol is briefly introduced as follows, and please refer to [13] for a more detailed description. 43 healthy participants (26.35 ± 2.89 years, 23 M, 20 F) participated in the experiment.…”

Section: A Grabmyo Datasetmentioning

confidence: 99%

Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication

Fu,

Pradhan,

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.

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“…The electrode arrangements are a promising technology that is widely used for transcutaneous electrical stimulation (TES). It has been approved that the dynamic adaptation of electrode size and position helps to simplify the use of electrical stimulation systems and to increase their clinical efficacy [8]- [10]. However, it is still unclear how the electrode size affects the current distribution of the target muscle.…”

Section: Electrode Design Proceduresmentioning

confidence: 99%

A Finite Element Approach for Forearm Neuromodulation: Impact of Electrode Size on the Current Density

Salkim

2024

J. Phys.: Conf. Ser.

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Electrical impedance myography is a transcutaneous neuromodulation method for assessing muscle conditions through the application of a high-frequency, low-intensity current to the muscle region of interest (ROI). It has been shown that the mechanisms underpinning these findings are controversial as studies showed that the current reaching the target structure may not be enough to activate tissue due to various factors. It has been shown that anatomical properties as well as non-anatomical factors including electrode shape and size, inter-electrode distance may affect the outcome. This study was conducted to investigate the impact of the different sizes of the electrodes on the current density of the ROI. It may not be feasible to investigate these parameters impact on the outcome using experimental procedures. Alternatively, the computational methos have been used as a tool to study electrical stimulation of bio-computational models. The neuromodulators can be designed and developed using such advanced methods. This study investigates the impact of the electrode size on the current distributions. The fundamental anatomical layers of the human forearm were generated based on standard dimensions using concentric shapes. A sinusoidal bipolar current pulse was applied on the different sizes of electrodes to simulate current distribution within the associated anatomical layers. It was shown that the electrode size has a significant impact on the induced current density of the target anatomical layer.

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Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics

Cited by 17 publications

References 36 publications

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication

A Finite Element Approach for Forearm Neuromodulation: Impact of Electrode Size on the Current Density

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