The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A state-based approach to the representation and recognition of gesture
Abstract
A state-based technique for the representation and recognition of gesture is presented. We define a gesture to be a sequence of states in a measurement or configuration space. For a given gesture, these states are used to capture both the repeatability and variability evidenced in a training set of example trajectories. Using techniques for computing a prototype trajectory of an ensemble of trajectories, we develop methods for defining configuration states along the prototype and for recognizing gestures from an unsegmented, continuous stream of sensor data. The approach is illustrated by application to a range of gesture-related sensory data: the two-dimensional movements of a mouse input device, the movement of the hand measured by a magnetic spatial position and orientation sensor, and, lastly, the changing eigenvector projection coefficients computed from an image sequence.
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
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Recognizing human action in time-sequential images using hidden Markov model
Yamato, J., Ohya, J., & Ishii, K. (2003) · Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1992.223161 · Cited by 1,273
Cited by
A survey on behavior analysis in video surveillance for homeland security applications
Ko, T. (2008) · 2008 37th IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2008.4906450 · Cited by 209
Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic
Su, C.-J., Chiang, C.-Y., & Huang, J.-Y. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.04.020 · Cited by 149
Expectation grammars: leveraging high-level expectations for activity recognition
Minnen, D., Essa, I., & Starner, T. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211525 · Cited by 110
Metadata
- Authors
- Bobick, A. F., & Wilson, A. D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997
- Volume
- 19
- Issue
- 12
- Pages
- 1325-1337
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2134518303
- Cited by
- 281
- References
- 2
- FWCI
- 8.36
- 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 state-based approach to the representation and recognition of gesture. https://app.cochat.ai/papers/10.1109/34.643892
- The paper
- Bobick, A. F., & Wilson, A. D. (1997). A state-based approach to the representation and recognition of gesture. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.643892
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.
