The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors
Abstract
This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Myoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
Cited by
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 363
Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network
Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019) · Sensors · doi:10.3390/s19143170 · Cited by 165
Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors
Kundu, A. S., Mazumder, O., Lenka, P. K., & Bhaumik, S. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0725-0 · Cited by 152
Yu, L., Xiong, D., Guo, L., & Wang, J. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.02.012 · Cited by 134
A survey on artificial intelligence in Chinese sign language recognition
Jiang, X., Satapathy, S. C., Yang, L., Wang, S.-H., & Zhang, Y.-D. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04758-2 · Cited by 71
Metadata
- Authors
- Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., & Yang, J.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 2011-11
- Volume
- 41
- Issue
- 6
- Pages
- 1064-1076
- ISSN
- 1083-4427 (ISSN-L)
- 1558-2426
- OpenAlex
- W2108225567
- Cited by
- 618
- References
- 1
- FWCI
- 37.56
- Type
- journal article
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors. https://app.cochat.ai/papers/10.1109/tsmca.2011.2116004
- The paper
- Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., & Yang, J. (2011). A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. https://doi.org/10.1109/tsmca.2011.2116004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
