EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 565 (2024) E3S Web Conf., 565 (2024) 02022 References
Open Access
Issue
E3S Web Conf.
Volume 565, 2024
2024 5th International Conference on Urban Engineering and Management Science (ICUEMS2024)
Article Number 02022
Number of page(s) 4
Section Cultural Tourism Management and Business Innovation Development
DOI https://doi.org/10.1051/e3sconf/202456502022
Published online 09 September 2024
  1. T. Vaiyapuri, E. L. Lydia, M. Y. Sikkandar, V. G. Diaz, I. V. Pustokhina, and D. A. Pustokhin, “Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare,” IEEE Access, vol. 9, pp. 113879–113888, 2021. [CrossRef] [Google Scholar]
  2. F. Sabry, T. Eltaras, W. Labda, F. Hamza, K. Alzoubi, and Q. Malluhi, “Towards on-device dehydration monitoring using machine learning from wearable device’s data,” Sensors, vol. 22, no. 5, 2022. [Google Scholar]
  3. A. B. A. Pinto, G. A. de Assis, L. C. B. Torres, T. Beltrame, and D. M. G. Domingues, “Wearables and Detection of Falls: A. Comparison of Machine Learning Methods and Sensors Positioning,” Neural Processing Letters, vol. 54, no. 3, pp. 2165–2179, 2022. [CrossRef] [Google Scholar]
  4. Z. Liu, M. Yang, Y. Yuan, and K. Y. Chan, “Fall Detection and Personnel Tracking System Using Infrared Array Sensors,” IEEE Sensors Journal, vol. 20, no. 16, pp. 9558–9566, Aug. 15, 2020, DOI: 10.1109/JSEN.2020.2988070. [Google Scholar]
  5. P. Youngkong and W. Panpanyatep, “A Novel Double Pressure Sensors-Based Monitoring and Alarming System for Fall Detection,” in 2021 Second International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), Bangkok, Thailand, 2021, pp. 1–5, DOI: 10.1109/ICA-SYMP50206.2021.9358439. [Google Scholar]
  6. D. Zhang, H. Zhang, and C. Zhao, “Prediction of behavior and activity of patients through wearable devices based on deep learning,” Journal of Sensors, vol. 2022, Article ID 3067840, 12 pages, 2022. [Google Scholar]
  7. X. Chen, H. Xue, M. Kim, C. Wang, and H. Y. Youn, “Detection of Falls with Smartphone Using Machine Learning Technique,” in 2019 8th International Congress on Advanced Applied Informatics (IIAI- AAI), Toyama, Japan, 2019, pp. 611–616, DOI: 10.1109/IIAI-AAI.2019.00129. [CrossRef] [Google Scholar]
  8. W.-J. Chang, L.-B. Chen, J.-P. Su, M.-C. Chen, and T.-C. Yang, “A Fall Risk Prediction System Based on 3D Space Human Skeleton Torso Images,” in 2021 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2021, pp. 1–2, DOI: 10.1109/ICCE50685.2021.9427640. [Google Scholar]
  9. A. Chandak, N. Chaturvedi, and D. Dhiraj, “MachineLearning-Based Human Fall Detection Using Contact- and Noncontact-Based Sensors,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–15, 2022. [Google Scholar]
  10. F.-Y. Lin, P.-T. Lee, Y.-H. Ho, P.-S. Sung, P.-T. Chen, and C.-L. Lin, “Fall Prediction Based on HeadMounted IMU Sensor System,” in 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE), Osaka, Japan, 2022, pp. 341–343, DOI: 10.1109/GCCE56475.2022.10014374. [CrossRef] [Google Scholar]
  11. Y. Shen, M. Voisin, A. Aliamiri, A. Anand, A. Hannun, and A. Ng, “Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, KDD ‘19, pp. 1909–1916, Association for Computing Machinery, New York, NY, USA, July, 2019. [CrossRef] [Google Scholar]
  12. Stanley, J.A.; Johnsen, S.B.; Apfeld, J. The SensorOverlord predicts the accuracy of measurements with ratiometric biosensors. Sci. Rep. 2020, 10, 1–11. [NASA ADS] [CrossRef] [Google Scholar]
  13. G. Shi, T. Zhang, Y. Jin, et al., “Algorithm of the fall prediction based on the double foot pressure and Micro Inertial sensors,” in 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Chengdu, China, 2016, pp. 354–359, DOI: 10.1109/CYBER.2016.7574849. [Google Scholar]
  14. N. Santesso, A. Carrasco-Labra, R. Brignardello-Petersen, “Hip protectors for preventing hip fractures in older people,” Cochrane Database of Systematic Reviews, 2014, DOI: 10.1002/14651858.CD001255.pub5. [Google Scholar]
  15. L. Day, K. D. Hill, V. Z. Stathakis, et al., “Impact of Tai-Chi on Falls Among Preclinically Disabled Older People. A. Randomized Controlled Trial,” Journal of the American Medical Directors Association, vol. 16, no. 5, 2015. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.