Classification of Electromyographic Hand Gesture Signals using Machine Learning Techniques
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [122]
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References 60 · Cited by 89
- Authors
- Jia, G., Lam, H.-K., Liao, J., & Wang, R.
- Affiliations
- King's College London (GB)
- Corresponding author
- Lam, Hak-Keung
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 401
- Pages
- 236-248
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3011275915
- Cited by
- 89
- References
- 60
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 23 · 2023: 16 · 2022: 13 · 2021: 16 · 2020: 4
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- China Scholarship Council
- Engineering and Physical Sciences Research Council
- King's College London
- 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 classification, convolutional autoencoder, convolutional neural networks, latent representations, window method, majority voting, noise robustness, motion intention recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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