TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

A review on monitoring activities through human activity recognition

, , , , , , *

* Corresponding author · click or hover a name for details

pp. 533–543Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1591XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1591
Pages:
533–543

Abstract

The field of human activity recognition (HAR) is advancing with the integration of cutting-edge technologies such as artificial intelligence and sensor networks. These developments enable accurate detection and interpretation of human behavior, driven by advances in AI and machine learning. A growing number of electronic devices and applications contribute to a comprehensive understanding of the three pillars of HAR – AI, various devices and methodologies and their applications. This review consolidates HAR research and highlights its processes, applications, and device comparisons. Key findings include the importance of non-invasive, real-time sensor-based approaches, particularly in healthcare for the detection of behavioral abnormalities in the elderly. The study highlights Adam as an outstanding optimizer in HAR applications, who predicts continued expansion and development in various industries.particularly in the healthcare sector.

Keywords

Subject Classifications

68-XX94-XX

References

[1] Pham, T., Wang, J., & Bui, V. N., A wearable sensor-based system for activity recognition and fall detection. Sensors, 20(10), 2899 (2020).
[2] Wang, T., Chen, C., & Zhang, W., Real-time crowd anomaly detection and identification using 3D skeleton data from depth cameras. IEEE Transactions on Human-Machine Systems, 49(1), 119-129 (2018).
[3] Chen K, Zhang D, Yao L, Guo B, Yu Z, & Liu Y. Deep learning for sensor-based human activity recognition: overview, challenges and opportunities. arXiv, vol. 37, no. 4 (2020). 
[4] Du, S., He, L., & Zhou, X. A survey of RFID-based activity recognition systems for elderly care. ACM Computing Surveys (CSUR), 52(2), 1-35 (2019).
[5] Yao, C., Yang, X., & He, X., An approach to fall detection using Wi-Fi signals. Sensors, 18(6), 1908 (2018).
[6] L. Wang et al. Unsupervised Anomaly Detection in Surveillance Videos via Deep Metric Learning (2019).
[7] Sathyanarayana, A., Ofli, F., Fernandes-Luque, L., Srivastava, J., Elmagarmid, A., Arora, T., & Taheri, S., Robust Automated Human Activity Recognition and its Application to Sleep Research (2016).
[8] Y. Sun et al. Adaptive Exercise Recommendation and Feedback System Using Deep Learning Techniques (2021).
[9] S.M. Debnath et al. Human Activity Recognition from Smartphone Sensor Data for Mental Health Assessment (2022).
[10] Arifoglu, D., Wang, Y., & Bouchachia, A. Detection of Dementia-Related Abnormal Behavior Using Recursive Auto-Encoders. Sensors, 21(1), 260 (2021).
[11] Abobakr, M. M., El-Baz, A., & El-Sallam, A. A., Real-time human activity recognition using depth cameras and convolutional neural networks. Pattern Recognition Letters, 116, 53-61 (2018).

Views: 306Downloads: 78Citations: 1