A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Laviolette, François; Gosselin, Benoît

doi:10.3390/s19122811

Cited by 58 publications

(53 citation statements)

References 37 publications

(75 reference statements)

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“…As stated in Côté- Allard et al (2019b), the data acquisition protocol was approved by the Comités d'Éthique de la Recherche avec desêtres humains de l'Université Laval, and informed consent was obtained from all participants.…”

Section: Emg Datamentioning

confidence: 99%

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Côté-Allard

Campbell

Phinyomark

et al. 2020

Front. Bioeng. Biotechnol.

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The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training.Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.

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Section: Emg Datamentioning

confidence: 99%

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Côté-Allard

Campbell

Phinyomark

et al. 2020

Front. Bioeng. Biotechnol.

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“…This work employs the 3DC Dataset [26] for architecture building and hyperparameter optimization and the Longterm 3DC Dataset [12] for training and testing the different algorithms considered. Both datasets were recorded using the 3DC Armband [26]; a wireless, 10-channel, dry-electrode, 3D printed sEMG armband. The device samples data at 1000 Hz per channel, allowing to take advantage of the full spectra of sEMG signals [27].…”

Section: Datasets and Network's Architecturementioning

confidence: 99%

“…As stated in [12], [26], the data acquisition protocol of the 3DC Dataset and Long-term 3DC Dataset were approved by the Comités d'Éthique de la Recherche avec des êtres humains de l'Université Laval (approval number: 2017-0256 A-1/10-09-2018 and 2017-026 A2-R2/26-06-2019 respectively), and informed consent was obtained from all participants.…”

Section: Datasets and Network's Architecturementioning

confidence: 99%

“…Finally, following [29], the time and channel axis are swapped such that an example is of the shape 4 × 10 × 24 (time × channel × f requency). Spectrograms were selected as inputs for the ConvNet presented in Section III-C, as they have been shown to obtain competitive performance on a wide variety of datasets [6], [18], [26] and in the control of a robotic arm in real-time [14]. In addition, they are relatively inexpensive to compute and allow for faster training of a ConvNet when compared to the raw sEMG signal due to the relatively low dimensionality of the obtained input images from the spectrograms.…”

Section: ) Data Pre-processingmentioning

confidence: 99%

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Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

et al. 2020

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Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art selfcalibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

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“…Regarding Libras, it is necessary to point out that image-approach was the tool for several studies for SLRs [33][34][35]. The sensor approach is based on instrumented gloves, and sEMG wearable devices are a field that is being explored in recent works, achieving high accuracies and hit rates [36,37]. There is a limited number of works combining sEMG and Libras, and their focus is on alphabet classification.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Mendes

Freitas

Campos

et al. 2020

Sensors

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Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

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A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Cited by 58 publications

References 37 publications

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

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