The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Comparative study on classifying human activities with miniature inertial and magnetic sensors
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Citations
Cites
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“For several decades, inertial sensors have been used for navigation of aircraft [1,2], ships, land vehicles, and robots [3–5], for state estimation and dynamic modeling of legged robots [6,7], for shock and vibration analysis in the automotive industry, and in telesurgery [8,9].”
Background
Cited by
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
“6 50–200 Hz 37 Daily & Sports Activities [19] Locomotion 9D IMU 19 25 Hz 37 MHEALTH [30, 31] Locomotion & gesture 9D IMU & ECG 12 50 Hz 33 Opportunity [59] Locomotion & gesture 9D IMU 16 50 Hz 32 PAMAP2 [228] Locomotion & activities 9D IMU & HR monitor 18 100 Hz 32 Daphnet [23] Freezing of gait 3D Acc.”
BackgroundMethodDemrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368
Wearable Inertial Sensors for Human Motion Analysis: A Review
Lopez-Nava, I. H., & Munoz-Melendez, A. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2609392 · Cited by 263
A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition
Janidarmian, M., Roshan Fekr, A., Radecka, K., & Zilic, Z. (2017) · Sensors · doi:10.3390/s17030529 · Cited by 217
Benchmarking TinyML Systems: Challenges and Direction
Banbury, C. R., Reddi, V. J., Lam, M., Fu, W., Fazel, A., Holleman, J., Huang, X., Hurtado, R., Kanter, D., Lokhmotov, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2003.04821 · Cited by 216
Analysis of animal accelerometer data using hidden Markov models
Leos‐Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M., & Papastamatiou, Y. P. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12657 · Cited by 187
Application of data fusion techniques and technologies for wearable health monitoring
King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.12.011 · Cited by 182
Chamroukhi, F., Mohammed, S., Trabelsi, D., Oukhellou, L., & Amirat, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.003 · Cited by 88
Huynh-The, T., Hua, C.-H., Tu, N. A., & Kim, D.-S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3013272 · Cited by 72
Deep Fisher discriminant learning for mobile hand gesture recognition
Li, C., Xie, C., Zhang, B., Chen, C., & Han, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.023 · Cited by 72
Chan, S., Hang, Y., Tong, C., Acquah, A., Schonfeldt, A., Gershuny, J., & Doherty, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03960-3 · Cited by 61
(2020) · doi:10.1145/3380999 · Cited by 57
Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks
Zhang, L., Wu, X., & Luo, D. (2015) · 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2015.48 · Cited by 59
Sanabria, A. R., Zambonelli, F., Dobson, S., & Ye, J. (2021) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2021.101477 · Cited by 43
Yurtman, A., & Barshan, B. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2930437 · Cited by 26
Self-citationA GAN-based data augmentation method for human activity recognition via the caching ability
Shi, J., Zuo, D., & Zhang, Z. (2021) · Internet Technology Letters · doi:10.1002/itl2.257 · Cited by 24
Optimizing computational resources for edge intelligence through model cascade strategies
Gomez-Carmona, O., Casado-Mansilla, D., Lopez-de-Ipina, D., & Garcia-Zubia, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3118845 · Cited by 21
Balestra, N., Sharma, G., Riek, L. M., & Busza, A. (2021) · Digital Biomarkers · doi:10.1159/000516619 · Cited by 13
Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains
Molino-Minero-Re, E., Aguileta, A. A., Brena, R. F., & Garcia-Ceja, E. (2021) · Sensors · doi:10.3390/s21217007 · Cited by 11
On the Interpretation of 3D Gyroscope Measurements
Stančin, S., & Tomažič, S. (2018) · Journal of Sensors · doi:10.1155/2018/9684326 · Cited by 10
UniMTS: Unified Pre-training for Motion Time Series
Zhang, X., Teng, D., Chowdhury, R., Li, S., Hong, D., Gupta, R., & Shang, J. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-3414 · Cited by 8
UniMTS: Unified Pre-training for Motion Time Series
Zhang, X., Teng, D., Chowdhury, R. R., Li, S., Hong, D., Gupta, R. K., & Shang, J. (2024) · arXiv · Cited by 9
“Following the same Wilcoxon-signed rank test as in the zero-shot setting, we observe p-values far below 0.05 (e.g., p-value = 2 × 10 − 25 for the best-performing baseline ImageBind), indicating the statistical significance of our improvement.”
Influential
Metadata
- Authors
- Altun, K., Barshan, B., & Tunçel, O.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 43
- Issue
- 10
- Pages
- 3605-3620
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2123504417
- Cited by
- 564
- References
- 3
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- green
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
