The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
MobiGesture: Mobility-aware hand gesture recognition for healthcare
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“For example, a wrist-worn wearable outfitted with an inertial measurement unit (IMU) can easily detect the wrist shaking [47,48,49] to control smart devices to skip a song by shaking the hand, instead of bringing up the screen, locating, and pushing a button.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [47]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(1992) · Cited by 23,431
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
de Boor, C. (1978) · Applied mathematical sciences · doi:10.1007/978-1-4612-6333-3 · Cited by 12,085
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. J. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,954
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
The Importance of Walking to Public Health
Lee, I.-M. M., & Büchner, D. M. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31817c65d0 · Cited by 624
Gesture spotting with body-worn inertial sensors to detect user activities
Junker, H., Amft, O., Lukowicz, P., & Tröster, G. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.11.016 · Cited by 361
Older Adults, Chronic Disease and Leisure-Time Physical Activity
Ashe, M. C., Miller, W. C., Eng, J. J., & Noreau, L. (2008) · Gerontology · doi:10.1159/000141518 · Cited by 346
Simonsick, E. M., Guralnik, J. M., Volpato, S., Balfour, J. L., & Fried, L. P. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53103.x · Cited by 301
Parate, A., Chiu, M., Chadowitz, C., Ganesan, D., & Kalogerakis, E. (2014) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2594368.2594379 · Cited by 282
Ability to Walk 1/4 Mile Predicts Subsequent Disability, Mortality, and Health Care Costs
Hardy, S. E., Kang, Y., Studenski, S. A., & Degenholtz, H. B. (2011) · Journal of General Internal Medicine · doi:10.1007/s11606-010-1543-2 · Cited by 280
A trivariate clough—tocher scheme for tetrahedral data
Alfeld, P. (1984) · Computer Aided Geometric Design · doi:10.1016/0167-8396(84)90029-3 · Cited by 266
Finger-writing with Smartwatch
Xu, C., Pathak, P. H., & Mohapatra, P. (2015) · Proceedings · doi:10.1145/2699343.2699350 · Cited by 238
A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking
Dong, Y., Hoover, A. W., Scisco, J. L., & Muth, E. R. (2012) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-012-9194-1 · Cited by 231
Chan, L., Liang, R.-H., Tsai, M.-C., et al. (2013) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2501988.2502016 · Cited by 181
Chen, K.-Y., Lyons, K., White, S., & Patel, S. (2013) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2501988.2502035 · Cited by 170
Physical Activity, Public Health, and Aging: Critical Issues and Research Priorities
PROHASKA, T. R., Belansky, E. S., Belza, B. L., Büchner, D. M., Marshall, V. W., McTigue, K. M., Satariano, W. A., & Wilcox, S. (2006) · The Journals of Gerontology Series B · doi:10.1093/geronb/61.5.s267 · Cited by 158
Chen, K.-Y., Patel, S., & Keller, S. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858125 · Cited by 153
Park, T., Lee, J., Hwang, I., et al. (2011) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2070942.2070969 · Cited by 119
An Inertial Measurement Framework for Gesture Recognition and Applications
Benbasat, A. Y., & Paradiso, J. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47873-6_2 · Cited by 85
A smart watch-based gesture recognition system for assisting people with visual impairments
Porzi, L., Messelodi, S., Modena, C. M., & Ricci, E. (2013) · doi:10.1145/2505483.2505487 · Cited by 73
Self-contained spatial input device for wearable computers
Bang, W.-C., Chang, W., Kang, K.-H., et al. (2004) · Proceedings · doi:10.1109/iswc.2003.1241390 · Cited by 71
A recognition method for combined activities with accelerometers
Murao, K., & Terada, T. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2638728.2641304 · Cited by 24
Cited by
Sorted by citations
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
“For example, a wrist-worn wearable outfitted with an inertial measurement unit (IMU) can easily detect the wrist shaking [250, 324, 336] to control smart devices to skip a song by shaking the hand, instead of bringing up the screen, locating, and pushing a button.”
Background
Metadata
- Authors
- Zhao, H., Ma, Y., Wang, S., Watson, A., & Zhou, G.
- Affiliations
- William & Mary (US)
- Corresponding author
- Zhao, Hongyang
- Venue
- Smart Health
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 9-10
- Pages
- 129-143
- ISSN
- 2352-6483 (ISSN-L)
- OpenAlex
- W2839717666
- Cited by
- 18
- References
- 24
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 4 · 2022: 3 · 2020: 3
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Smart Health (publishedVersion)
- Funders
- NSF (CNS-1253506)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- hand gesture recognition, gesture segmentation, gesture classification, convolutional neural networks, medical applications, wearable sensors, walking and jogging
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
