Improving sEMG-Based Hand Gesture Recognition Using Maximal Overlap Discrete Wavelet Transform and an Autoencoder Neural Network
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Autoencoders are most commonly used for feature extraction and dimensionality reduction.
“As such, autoencoders are most commonly used for feature extraction and dimensionality reduction [120,122,123,124,125,126,133,134,135,136,137,138,139,140,141].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [135]
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“As such, autoencoders are most commonly used for feature extraction and dimensionality reduction [18, 27, 53, 73, 74, 80, 103, 137, 171, 172, 194, 234, 283, 287, 296, 298].”
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- Authors
- de Andrade, F. H. C., Pereira, F. G., Resende, C. Z., & Cavalieri, D. C.
- Affiliations
- Instituto Federal do Espírito Santo (BR)
- Corresponding author
- Cavalieri, Daniel Cruz
- Venue
- IFMBE Proceedings
- Publisher
- Springer Singapore
- Published
- 2019
- Pages
- 271-279
- ISSN
- 1433-9277 (ISSN-L)
- 1680-0737
- OpenAlex
- W2946263433
- Cited by
- 8
- References
- 29
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 3 · 2021: 2 · 2019: 1
- FWCI
- 3.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- surface electromyography, hand gesture recognition, maximal overlap discrete wavelet transform, autoencoder, time-domain features, linear discriminant analysis, precision grip, power grip, grasp classification, feature extraction
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