Elderly People's Abnormal Behavior Detection Using HAR and CNN Algorithms.

Authors

  • Sneha Bajirao Paymal Research Scholar, RIT, Islampur, 415409, India
  • Mahadev S. Patil Peofessor, HOD of the E&TC Department, RIT Islampur, City, 415409, India

Keywords:

Convolutional Neural network (CNN), Human Activity Recognition (HAR), Deep Learning (DL), firebase, IOT, OpenCV

Abstract

Artificial intelligence machine learning systems are developing rapidly and very fast with the period with increasing demand and dependencies on them. New developments have been made for Artificial Intelligence and machine learning has made artificial brains for detection, identification, and decision-making abilities available for computer machines. The paper proposes the recognition of human health prediction by recognizing abnormal actions, or signs given by humans using OPENCV CNN, a tensor flow platform comparing live actions with different action datasets stored. The system detects falls of a person, sleep, heart pain, stomach pain, shoulder pain, dizziness, and different actions related to daily routine such as exercise, reading, writing, playing, makeup, etc recognize actions are sent to the Firebase cloud platform to be monitored by user or user recommended physician. Abnormal action will provide a warning message for help or raise an alarm for help. The system can detect action using surveillance cameras, or Pi camera, or a webcam.

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References

Abdellaoui, M., Douik, A., “Human action recognition in video sequences using deep belief networks”, Traitement du Signal, Vol. 37, No. 1, https://doi.org/10.18280/ts.370105, pp. 37-44,2020

Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David A. Ross, Jia Deng, Rahul Sukthankar; “Rethinking the Faster R-CNN Architecture for Temporal Action Localization”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 1130-1139.

Feichtenhofer, Christoph & Fan, Haoqi & Malik, Jitendra & He, Kaiming,” Slow Fast Networks for Video Recognition” 2018.

Ding, Chunhui & Fan, Shouke & Zhu, Ming & Weiguo, Feng & Jia, Baozhi.,” Violence Detection in Video by Using 3D Convolutional Neural Networks”, 8888. 10.1007/978-3-319-14364-4_53. Pp. 551-558.2014.

Karpathy, Andrej & Toderici, George & Shetty, Sanketh & Leung, Thomas & Sukthankar, Rahul & Fei-Fei, Li. “Large-Scale Video Classification with Convolutional Neural Networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition”, pp. 1725-1732. 101109/CVPR.2014.223.

S. Z. Gurbuz and M. G. Amin, ‘‘Radar-based human-motion recognition with deep learning: Promising applications for indoor monitoring,’’ IEEE Signal Process. Mag., vol. 36, no. 4, pp. 16– 28, Jul. 2019.

Y. Kim and H. Ling, ''Human activity classification based on micro doppler signatures using an artificial neural network,’’ in Proc. IEEE Antennas Propag. Soc.Int. Symp., Jul. 2008.

Jalal A, Kamal S, Kim D. A Depth “Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments”. Sensors. 2014;14(7): 1173511759.https://doi.org/10.3390/s140711735.

J. Li, S. L. Phung, F. H. C. Tivive, and A. Bouzerdoum,'' Automatic classification of human motions using Doppler radar,'' in Proc. Int. Joint Conf. Neural Netw. (IJCNN), pp.1– 6, Jun. 2012,

MW. Li, B. Xiong, and G. Kuang, ''Target classification and recognition based on micro-Doppler radar signatures,'' in Proc. Progr. Electromagn. Res. Symp. -FALL (PIERS-FALL), pp. 1679–1684, Nov. 2017,

https://www.deepmind.com/open-source/kinetics

Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. W. Baik, "Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features," in IEEE Access, vol. 6, pp. 1155-1166, 2018, doi: 10.1109/ACCESS.2017.2778011

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Published

29.01.2024

How to Cite

Paymal, S. B. ., & Patil, M. S. . (2024). Elderly People’s Abnormal Behavior Detection Using HAR and CNN Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 646 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4629

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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Copyright (c) 2024 Sneha Bajirao Paymal, Mahadev S. Patil

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