Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
By Yu Du, Wenguang Jin, Wentao Wei, Yu Hu, Wei-dong Geng. Italian National Conference on Sensors, 2017
351 citations recorded by the source corpus.
High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.
Subfields: Biomedical Engineering, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/a22d48b6046369e24f2f0e55f73a99ffc9c41a05
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Daohui Zhang
- Alexandre Campeau-Lecours
- U. Côté-Allard
- Clément M. Gosselin
- Alexandre Drouin
- Xingang Zhao
- Dezhen Xiong
- Yiwen Zhao
In Cognitive Neuroscience
- Alexandre Campeau-Lecours
- U. Côté-Allard
- Clément M. Gosselin
- Alexandre Drouin
- Kyrre Harald Glette
- François Laviolette
- Benoit Gosselin
- Cheikh Latyr Fall
Citations in the corpus
- Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “As usual in deep learning, the architecture was created in a trial and error process taking inspiration from previous architectures (primarily [4], [6], [47], [7]).”
- Deep Learning for EMG-based Human-Machine Interaction: A Review (2021, methodology): “Deep transfer learning has been applied to object detection [45], [46], image classification [47], [48], and so on.”