Human Activity Recognition (HAR) in Healthcare, 2nd Edition
Source abstract
Abstract
Technological advances, particularly in the medical field, have significantly improved patients’ quality of life [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Context-Aware Activity Recognition Systems · IoT and Edge/Fog Computing · Healthcare Technology and Patient Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Energy, Environment and Materials (DICEAM), Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, Italy
Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, NTNU/Norwegian University of Science and Technology, 7034 Trondheim, Norway
References (15)
Show source-provided references
1. Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions10.1007/s11831-023-09986-x ↗
2. A Systematic Literature Review on Human Activity Recognition10.52783/jes.2848 ↗
3. Deep Learning for Sensor-Based Activity Recognition: A Survey10.1016/j.patrec.2018.02.010 ↗
4. Hammerla, N.Y., Halloran, S., and Ploetz, T. (2023). Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables. arXiv.
5. Reyes-Ortiz, J.L., Anguita, D., Ghio, A., Oneto, L., and Parra, X. (2012). Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly SVM. Ambient Assisted Living and Home Care, Proceedings of the 4th International Workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, 3–5 December 2012, Springer.
6. Ordóñez, F.J., and Roggen, D. (2023). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.10.3390/s16010115 ↗
7. Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
8. A Survey on Human Activity Recognition Using Wearable Sensors10.1109/SURV.2012.110112.00192 ↗
9. Human Activity Recognition with Smartphone Sensors Using Deep Learning Neural Networks10.1016/j.eswa.2016.04.032 ↗
10. Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6–7). Convolutional Neural Networks for Human Activity Recognition Using Mobile Sensors. Proceedings of the IEEE Transactions on Mobile Computing, Austin, TX, USA.10.4108/icst.mobicase.2014.257786 ↗
11. Hafeez, S. (2022). Robust Vital-Sign Monitoring of Human Attention Through Deep Multi-Modal Sensor Fusion. [Ph.D. Thesis, Air University].
12. Deep learning for computer vision-based activity recognition and fall detection of the elderly: A systematic review10.1007/s10489-024-05645-1 ↗
13. Guerra, B.M.V., Torti, E., Marenzi, E., Schmid, M., Ramat, S., Leporati, F., and Danese, G. (2023). Ambient assisted living for frail people through human activity recognition: State-of-the-art, challenges and future directions. Front. Neurosci., 17.10.3389/fnins.2023.1256682 ↗
14. Fiska, V., Mitsopoulos, K., Mantiou, V., Petronikolou, V., Antoniou, P., Tagaras, K., Kasimis, K., Nizamis, K., Tsipouras, M.G., and Astaras, A. (2025). Integration and Validation of Soft Wearable Robotic Gloves for Sensorimotor Rehabilitation. Appl. Sci., 15.10.3390/app15105299 ↗
15. Bibbò, L., Angiulli, G., Laganà, F., Pratticò, D., Cotroneo, F., La Foresta, F., and Versaci, M. (2025). MEMS and IoT in HAR: Effective Monitoring for the Health of Older People. Appl. Sci., 15.10.3390/app15084306 ↗