56 research outputs found
The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface
http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
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.Comment: The first two authors shared first authorship. The last three authors
shared senior authorship. 32 page
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
Within the field of electromyography-based (EMG) gesture recognition,
disparities exist between the offline accuracy reported in the literature and
the real-time usability of a classifier. This gap mainly stems from two
factors: 1) The absence of a controller, making the data collected dissimilar
to actual control. 2) The difficulty of including the four main dynamic factors
(gesture intensity, limb position, electrode shift, and transient changes in
the signal), as including their permutations drastically increases the amount
of data to be recorded. Contrarily, online datasets are limited to the exact
EMG-based controller used to record them, necessitating the recording of a new
dataset for each control method or variant to be tested. Consequently, this
paper proposes a new type of dataset to serve as an intermediate between
offline and online datasets, by recording the data using a real-time
experimental protocol. The protocol, performed in virtual reality, includes the
four main dynamic factors and uses an EMG-independent controller to guide
movements. This EMG-independent feedback ensures that the user is in-the-loop
during recording, while enabling the resulting dynamic dataset to be used as an
EMG-based benchmark. The dataset is comprised of 20 able-bodied participants
completing three to four sessions over a period of 14 to 21 days. The ability
of the dynamic dataset to serve as a benchmark is leveraged to evaluate the
impact of different recalibration techniques for long-term (across-day) gesture
recognition, including a novel algorithm, named TADANN. TADANN consistently and
significantly (p<0.05) outperforms using fine-tuning as the recalibration
technique.Comment: 10 Pages. The last three authors shared senior authorshi
A transferable adaptive domain adversarial neural network for virtual reality augmented EMG-Based gesture recognition
Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique
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