The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition
Abstract
We present Tomo, a wearable, low-cost system using Electrical Impedance Tomography (EIT) to recover the interior impedance geometry of a user's arm. This is achieved by measuring the cross-sectional impedances between all pairs of eight electrodes resting on a user's skin. Our approach is sufficiently compact and low-powered that we integrated the technology into a prototype wrist- and armband, which can monitor and classify gestures in real-time. We conducted a user study that evaluated two gesture sets, one focused on gross hand gestures and another using thumb-to-finger pinches. Our wrist location achieved 97% and 87% accuracies on these gesture sets respectively, while our arm location achieved 93% and 81%. We ultimately envision this technique being integrated into future smartwatches, allowing hand gestures and direct touch manipulation to work synergistically to support interactive tasks on small screens.
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
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Electrical Impedance Tomography
Holder, D. S., & Aristovich, K. Y. (2004) · CRC Press eBooks · doi:10.1201/9781003026242-9 · Cited by 815
Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor
Kim, D., Hilliges, O., Izadi, S., Butler, A. D., Chen, J., Oikonomidis, I., & Olivier, P. (2012) · Proceedings of the 25th annual ACM symposium on User interface software and technology · doi:10.1145/2380116.2380139 · Cited by 428
GestureWrist and GesturePad: unobtrusive wearable interaction devices
Rekimoto, J. (2002) · Proceedings Fifth International Symposium on Wearable Computers · doi:10.1109/iswc.2001.962092 · Cited by 410
Enabling always-available input with muscle-computer interfaces
Saponas, T. S., Tan, D. S., Morris, D., Balakrishnan, R., Turner, J., & Landay, J. A. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology · doi:10.1145/1622176.1622208 · Cited by 328
Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects
Sato, M., Poupyrev, I., & Harrison, C. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207743 · Cited by 322
Self-citationDemonstrating the feasibility of using forearm electromyography for muscle-computer interfaces
Saponas, T. S., Tan, D. S., Morris, D., & Balakrishnan, R. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357138 · Cited by 275
Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch
Xu, C., Pathak, P. H., & Mohapatra, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699350 · Cited by 235
WristFlex: low-power gesture input with wrist-worn pressure sensors
Dementyev, A., & Paradiso, J. A. (2014) · Proceedings of the 27th annual ACM symposium on User interface software and technology · doi:10.1145/2642918.2647396 · Cited by 203
Advancing Muscle-Computer Interfaces with High-Density Electromyography
Amma, C., Krings, T., Böer, J., & Schultz, T. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702501 · Cited by 168
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
Sensing Fine-Grained Hand Activity with Smartwatches
Laput, G., & Harrison, C. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300568 · Cited by 147
Self-citationDesign Guideline for Developing Safe Systems that Apply Electricity to the Human Body
Kono, M., Takahashi, T., Nakamura, H., Miyaki, T., & Rekimoto, J. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3184743 · Cited by 63
Metadata
- Authors
- Zhang, Y., & Harrison, C.
- Venue
- Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology
- Publisher
- ACM
- Published
- 2015-11-05
- Pages
- 167-173
- OpenAlex
- W2209204668
- Cited by
- 283
- References
- 18
- FWCI
- 26.49
- 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: Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition. https://app.cochat.ai/papers/10.1145/2807442.2807480
- The paper
- Zhang, Y., & Harrison, C. (2015). Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition. Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology. https://doi.org/10.1145/2807442.2807480
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.
