The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
uWave: Accelerometer-based personalized gesture recognition and its applications
Abstract
The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.
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
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods
Heumer, G., Amor, H. B., Weber, M., & Jung, B. (2007) · 2007 IEEE Virtual Reality Conference · doi:10.1109/vr.2007.352459 · Cited by 55
Cited by
Driving style recognition using a smartphone as a sensor platform
Johnson, D. A., & Trivedi, M. M. (2011) · 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2011.6083078 · Cited by 727
Preprocessing techniques for context recognition from accelerometer data
Figo, D., Diniz, P. C., Ferreira, D. R., & Cardoso, J. M. P. (2010) · Personal and Ubiquitous Computing · doi:10.1007/s00779-010-0293-9 · Cited by 578
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
Lu, Z., Chen, X., Li, Q., Zhang, X., & Zhou, P. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2302794 · Cited by 294
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
Metadata
- Authors
- Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., & Vasudevan, V.
- Venue
- 2009 IEEE International Conference on Pervasive Computing and Communications
- Publisher
- IEEE
- Published
- 2009-03
- Pages
- 1-9
- ISSN
- 2474-249X (ISSN-L)
- OpenAlex
- W2147780311
- Cited by
- 559
- References
- 2
- FWCI
- 30.10
- Type
- conference paper
- 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: uWave: Accelerometer-based personalized gesture recognition and its applications. https://app.cochat.ai/papers/10.1109/percom.2009.4912759
- The paper
- Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., & Vasudevan, V. (2009). uWave: Accelerometer-based personalized gesture recognition and its applications. 2009 IEEE International Conference on Pervasive Computing and Communications. https://doi.org/10.1109/percom.2009.4912759
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.
