The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A review on applications of activity recognition systems with regard to performance and evaluation
Open accessAbstract
Activity recognition systems are a large field of research and development, currently with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while increasing safety. This article concentrates on the applications of activity recognition systems and surveys their state of the art. We categorize such applications into active and assisted living systems for smart homes, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and tele-immersion applications. Within these categories, the applications are classified according to the methodology used for recognizing human behavior, namely, based on visual, non-visual, and multimodal sensor technology. We provide an overview of these applications and discuss the advantages and limitations of each approach. Additionally, we illustrate public data sets that are designed for the evaluation of such recognition systems. The article concludes with a comparison of the existing methodologies which, when applied to real-world scenarios, allow to formulate research questions for future approaches.
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Cites
Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living
Lawton, M. P., & Brody, E. M. (1969) · The Gerontologist · doi:10.1093/geront/9.3_part_1.179 · Cited by 18,540
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 2×
“Kwapisz et al.117 introduced a system which uses a phone-based accelerometer to recognize human activities.”
BackgroundKeeping the Resident in the Loop: Adapting the Smart Home to the User
Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2009.2025137 · Cited by 443
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
Vacher, M., Caffiau, S., Portet, F., Meillon, B., Roux, C., Elias, E., Lecouteux, B., & Chahuara, P. (2015) · ACM Transactions on Accessible Computing · doi:10.1145/2738047 · Cited by 73
Cited by
A systematic review of the smart home literature: A user perspective
Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.08.015 · Cited by 718
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Tang, C. I., Perez-Pozuelo, I., Spathis, D., Brage, S., Wareham, N., & Mascolo, C. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448112 · Cited by 140
Multi-user activity recognition: Challenges and opportunities
Li, Q., Gravina, R., Li, Y., Alsamhi, S. H., Sun, F., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.004 · Cited by 140
Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model
Luwe, Y. J., Lee, C. P., & Lim, K. M. (2022) · Informatics · doi:10.3390/informatics9030056 · Cited by 92
Chen, P.-W., Baune, N. A., Zwir, I., Wang, J., Swamidass, V., & Wong, A. W. K. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18041634 · Cited by 41
Metadata
- Authors
- Ranasinghe, S., Al Machot, F., & Mayr, H. C.
- Venue
- International Journal of Distributed Sensor Networks
- Publisher
- SAGE Publications
- Published
- 2016-08
- Volume
- 12
- Issue
- 8
- Pages
- 155014771666552
- ISSN
- 1550-1329 (ISSN-L)
- 1550-1477
- OpenAlex
- W2510575683
- Cited by
- 214
- References
- 5
- FWCI
- 11.87
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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