Authors : Dr. G. Indumathi; A. Dinesh Kumar Reddy; Anuvind Udayan Akral; M. Jaswanth

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/4973thwz

Scribd : https://tinyurl.com/34n9eswj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY232

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Accurate fall detection among older adults is crucial for minimizing injuries and fatalities. However, existing fall detection systems face challenges due to the rarity and variability of falls, compounded by limitations in real-world datasets. To address this, a novel fall detection approach integrating domain adaptation and context-awareness within a Coarse-Fine Network Learning framework is proposed. The model combines high-level semantic understanding with low-level spatial details to achieve robust fall detection across diverse environments. Domain adaptation techniques like transfer learning and domain-specific fine-tuning are introduced to enhance model generalization and adaptability. Additionally, context-aware features, including environmental cues and behavioral patterns, reduce false alarms. Extensive experimentation on real- world datasets demonstrates the superior performance of the model, outperforming traditional approaches. The framework holds promise for deployment in healthcare settings, contributing to improved safety for older adults worldwide. The interpretability of the model's predictions enhances its usability in practical applications.

Keywords : ADL :Activities Daily Living, CNN :Convolution Neural Network.

References :

1. Kaiqiang Huang,Susan Mckeever,Luis Mira les-Pechuán Generalized Zero-Shot Learning for Action Recognition Fusing Text and Image GANs IEEE Access, 2024
2. Junuk Cha,Muhammad Saqlain,Donguk Kim,Seungeun Lee,Seongyeong Lee,Seungryul Baek Learning 3D Skeletal Representation From Transformer for Action Recognition IEEE Access, 2022
3. Yun Han,Sheng-Luen Chung,Qiang Xiao,Wei You Lin,Shun-Feng Su Global Spatio-Temporal Attention for Action Recognition Based on 3D Human Skeleton Data IEEE Access, 2020
4. Nan Ma,Zhixuan Wu,Yiu-ming Cheung,Yuchen Guo,Yue Gao,Jiahong Li,Beijyan Jiang A Survey of Human Action Recognition and Posture Prediction Tsinghua Science and Technology, 2021
5. Jaeyeong Ryu,Ashok Kumar Patil,Bharatesh Chakravarthi,Adithya Balasubramanyam,Soungsi l Park,Youngho Chai Angular Features-Based Human Action Recognition System for a Real Application With Subtle Unit Actions IEEE Access, 2022
6. Chengwu Liang,Deyin Liu,Lin Qi,Ling Guan Multi-Modal Human Action Recognition With Sub-Action Exploiting and Class-Privacy Preserved Co laborative Representation Learning IEEE Access, 2022
7. Qinghua Li,Zhao Zhang,Yue You,Yaqi Mu,Chao Feng Data Driven Models for Human Motion Prediction in Human-Robot Co laboration IEEE Access, 2020
8. Weizhi Nie,Wei Wang,Xiangdong Huang SRNet: Structured Relevance Feature Learning Network From Skeleton Data for Human Action Recognition IEEE Access, 2019
9. K. Huang, L. Mira les-Pechuán and S. Mckeever, Enhancing zero-shot action recognition in videos by combining GANs with text and images, Social Netw. Comput. Sci., vol. 4, no. 4, pp. 375, May 2023.
10. A. Salazar, L. Vergara and G. Safont, Generative adversarial networks and Markov random fields for oversampling very sma l training sets, Expert Syst. Appl., vol. 163, Jan. 2021.
11.  H. Ding, Y. Ma, A. Deoras, Y. Wang and H. Wang, Zero-shot recommender systems, arXiv:2105.08318, 2021.
12. L. Wang, D. Q. Huynh and P. Koniusz, A comparative review of recent kinect-based action recognition algorithms, IEEE Trans. Image Process., vol. 29, pp. 15-28, 2020.
13.  J. Wang, Y. Chen, S. Hao, X. Peng and L. Hu, Deep learning for sensor-based activity recognition: A survey, Pattern Recognit. Lett., vol. 119, pp. 1-3, Mar. 2019.
14. A. U lah, K. Muhammad, I. U. Haq and S. W. Baik, Action recognition using optimized deep autoencoder and CNN for survei lance data streams of non-stationary environments, Future Gener. Comput. Syst., vol. 96, pp. 386-397, Jul. 2019.
15. IEEE Trans. Cognit. Develop. Syst., vol. 14, no. 1, pp. 246-252, Mar. 2022. J. Munro and D. Damen, Multi-modal domain adaptation for fine-grained action recognition, Proc. CVPR, pp. 122-132, Jun. 2020.
16.  X. Qin, Y. Ge, J. Feng, D. Yang, F. Chen, S. Huang, et al., DTMMN: Deep transfer multi-metric network for RGB-D action recognition, Neurocomputing, vol. 406, pp. 127-134, Sep. 2020.
17. H. Wang, Z. Song, W. Li and P. Wang, A hybrid network for large-scale action recognition from RGB and depth modalities, Sensors, vol. 20, no. 11, pp. 3305, Jun. 2020.
18. A. K.-F. Lui, Y.-H. Chan and M.-F. Leung, Mode ling of pedestrian movements near an amenity in walkways of public buildings, Proc. 8th Int. Conf. Control Autom. Robot. (ICCAR), pp. 394-400, Apr. 2022.
19. W. Cao, Z. Zhang, C. Liu, R. Li, Q. Jiao, Z. Yu, et al., Unsupervised discriminative feature learning via finding a clustering-friendly embedding space, Pattern Recognit., vol. 129, Sep. 2022.
20. Y.Jiang, D. K. Han and H. Ko, Relay dueling network for visual tracking with broad field-of-view, IET Comput. Vis., vol. 13, no. 7, pp. 615-622, Oct. 2019.
21. Y. Jin, J. Hong, D. Han and H. Ko, CPNet: Cross-para lel network for efficient anomaly detection, Proc. 17th IEEE Int. Conf. Adv. Video Signal Based Survei l. (AVSS), pp. 1-8, Nov. 2021.