ArticleSensors (Basel, Switzerland)2020
Data Augmentation of Surface Electromyography for Hand Gesture Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array.APL bioengineering · 2025Article
- CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.PNAS nexus · 2024Article
- Data augmentation for generating synthetic electrogastrogram time series.Medical & biological engineering & computing · 2024Article
- Machine Learning and Health Science Research: Tutorial.Journal of medical Internet research · 2024Article
- Robust myoelectric pattern recognition methods for reducing users' calibration burden: challenges and future.Frontiers in bioengineering and biotechnology · 2024Review
- A myoelectric digital twin for fast and realistic modelling in deep learning.Nature communications · 2023Article
- Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.Sensors (Basel, Switzerland) · 2022Article
- Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?Sensors (Basel, Switzerland) · 2021Article
- A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies-Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.