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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
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Issues up to 2022 co-published with and available at:
A review on monitoring activities through human activity recognition
Yogita Arorayogitamarora@gmail.comDepartment of Electronics and Communication Engineering Bharati Vidyapeeth College of EngineeringDepartment of Electronics & Communication Engineering Bharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile →
, Neha Guptaneha.gupta@suas.ac.inDepartment of Information Technology Bharati Vidyapeeth College of EngineeringDepartment of Computer Science and Information Technology Symbiosis University of Applied Sciences Indore, Madhya Pradesh, 110063, IndiaView full profile →
, Sarita Yadavsarita1320@yahoo.co.inDepartment of Information Technology Bharati Vidyapeeth College of EngineeringDepartment of Information Technology Bharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile →
, Neera AgarwalDepartment of Electronics and Communication Engineering Bharati Vidyapeeth College of EngineeringNew Delhi, IndiaView full profile →
, Tanusri Gururajtanusrigururaj04@gmail.comDepartment of Electronics and Communication Engineering Bharati Vidyapeeth College of EngineeringNew Delhi, IndiaView full profile →
, Tripti Chughtriptichugh310@gmail.comDepartment of Electronics and Communication Engineering Bharati Vidyapeeth College of EngineeringNew Delhi, IndiaView full profile →
, *Vipul AroraCorresponding authoraroravipul812@gmail.comDepartment of Electronics and Communication Engineering Bharati Vidyapeeth College of EngineeringNew Delhi, IndiaView full profile →
* Corresponding author · click or hover a name for details
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.
[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).
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