A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre; Scheme, Erik; Laviolette, François; Gosselin, Benoît

doi:10.1109/tnsre.2021.3059741

Cited by 28 publications

(20 citation statements)

References 46 publications

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“…Only three studies involved more than two of data collection days, but the number of subjects was smaller (<11) 12,17,18 . Only one study with three days of data recording, had a sample size of 20 subject, with only signals from the forearm 19 . To explore the robustness and accuracy of HGR and biometrics, it is imperative to have a database with larger subject pool sizes, recorded across multiple days and comprising numerous gestures.…”

Section: Background and Summarymentioning

confidence: 99%

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

Pradhan

Jiang

2022

Sci Data

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Surface electromyography (sEMG) signals have been used for advanced prosthetics control, hand-gesture recognition (HGR), and more recently as a novel biometric trait. For these sEMG-based applications, the translation from laboratory research setting to real-life scenarios suffers from two major limitations: (1) a small subject pool, and (2) single-session data recordings, both of which prevents acceptable generalization ability. In this longitudinal database, forearm and wrist sEMG data were collected from 43 participants over three different days with long separation (Days 1, 8, and 29) while they performed static hand/wrist gestures. The objective of this dataset is to provide a comprehensive dataset for the development of robust machine learning algorithms of sEMG, for both HGR and biometric applications. We demonstrated the high quality of the current dataset by comparing with the Ninapro dataset. And we presented its usability for both HGR and biometric applications. Among other applications, the dataset can also be used for developing electrode-shift invariant generalized models, which can further bolster the development of wristband and forearm-bracelet sensors.

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Section: Background and Summarymentioning

confidence: 99%

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

Pradhan

Jiang

2022

Sci Data

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“… Heatmap of the authors’ software classification against the performed tasks: serious games using abstract tasks [ 2 , 10 - 12 , 15 - 30 ], tasks related to ADL [ 10 , 31 ], and posture reproduction tasks [ 4 , 13 , 32 , 33 ]; as well as simulators using abstract tasks [ 34 - 36 ], ADL-related tasks [ 19 , 37 - 54 ], and posture reproduction tasks [ 56 - 67 ]. ADL: activities of daily living.…”

Section: Different Approachesmentioning

confidence: 99%

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Garske¹,

Dyson²,

Dupan³

et al. 2021

JMIR Serious Games

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Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training.

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“…2) EMG-Based Long-Term 3DC Dataset: The EMG-Based Long-Term 3DC Dataset [44] contains data from 20 ablebodied participants performing eleven hand gestures while recording their forearm's muscle activity over a period of 21 days (the recording sessions took place every ∼7 days). The goal for the unsupervised segmentation is to detect when a participant transitions towards a new gesture.…”

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confidence: 99%

Latent Space Unsupervised Semantic Segmentation

Strømmen¹,

Tørresen²,

Côté-Allard³

2022

Preprint

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The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify changepoints within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.

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A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

Cited by 28 publications

References 46 publications

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

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

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Latent Space Unsupervised Semantic Segmentation

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