A Novel Transfer Learning based Multi-Feature Fusion Framework for Elderly Activity Recognition
Keywords:
Elderly, Elderly activity recognition, HAR, Hand-Engineered FeaturesAbstract
One of the most interesting study areas in the broad spectrum of computer vision applications is human action recognition. Ambiguities in detecting actions stem from a variety of real-world issues, including camera motion, occlusion, noisy backgrounds, and the difficulty in defining the motion of body components. Elderly activity recognition systems have not received much attention from researchers, which pushes us to conduct serious study in this area. The literature has introduced a wide range of reliable ways, yet they are still insufficient to address the issues completely. The actions of older individuals differ from those of younger people for a variety of reasons, most notably health concerns, therefore the model created specifically for them cannot be tested on a dataset of younger people because the findings may vary in real-time. The proposed model combines self-learnt and hand-engineered features. This HAR model is more effective since the model takes into account two different features that were obtained using two different techniques. To evaluate these strategies' effectiveness both statistically and qualitatively, the proposed model has been evaluated on extensive performance metrics. It has been tested and validated with existing models on public benchmark datasets such as Stanford-40 Dataset.
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Chakraborty, S., Mondal, R., Singh, P. K., Sarkar, R., & Bhattacharjee, D. (2021). Transfer learning with fine tuning for human action recognition from still images. Multimedia Tools and Applications, 80, 20547-20578.
Sharma, V., Gupta, M., Pandey, A. K., Mishra, D., & Kumar, A. (2022). A review of deep learning-based human activity recognition on benchmark video datasets. Applied Artificial Intelligence, 36(1), 2093705.
Putra, P. U., Shima, K., & Shimatani, K. (2022). A deep neural network model for multi-view human activity recognition. PloS one, 17(1), e0262181.
Gupta, S., & Saini, A. K. (2013). Information system security and risk management: Issues and impact on organizations. Global Journal of Enterprise Information System, 5(1), 31-35.
Badawi, A. A., Al-Kabbany, A., & Shaban, H. (2018, December). Multimodal human activity recognition from wearable inertial sensors using machine learning. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 402-407). IEEE.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.
Sun, L., Jia, K., Chen, K., Yeung, D. Y., Shi, B. E., & Savarese, S. (2017). Lattice long short-term memory for human action recognition. In Proceedings of the IEEE international conference on computer vision (pp. 2147-2156).
Jindal, S., Sachdeva, M., & Kushwaha, A. K. S. (2022). Deep Learning for Video Based Human Activity Recognition: Review and Recent Developments. In Proceedings of International Conference on Computational Intelligence and Emerging Power System: ICCIPS 2021.
Nguyen, T. H. C., Nebel, J. C., & Florez-Revuelta, F. (2016). Recognition of activities of daily living with egocentric vision: A review. Sensors, 16(1), 72.
Gupta, N., Gupta, S. K., Pathak, R. K., Jain, V., Rashidi, P., & Suri, J. S. (2022). Human activity recognition in artificial intelligence framework: a narrative review. Artificial intelligence review, 1-54.
Gul, M. A., Yousaf, M. H., Nawaz, S., Ur Rehman, Z., & Kim, H. (2020). Patient monitoring by abnormal human activity recognition based on CNN architecture. Electronics, 9(12), 1993.
Khater, S., Hadhoud, M., & Fayek, M. B. (2022). A novel human activity recognition architecture: using residual inception ConvLSTM layer. Journal of Engineering and Applied Science, 69(1), 1-16.
Li, Y., & Wang, L. (2022). Human Activity Recognition Based on Residual Network and BiLSTM. Sensors, 22(2), 635.
Putra, P. U., Shima, K., & Shimatani, K. (2022). A deep neural network model for multi-view human activity recognition. PloS one, 17(1), e0262181.
Gorji, A., Bourdoux, A., Pollin, S., & Sahli, H. (2022). Multi-View CNN-LSTM Architecture for Radar-Based Human Activity Recognition. IEEE Access, 10, 24509-24519.
Ronald, M., Poulose, A., & Han, D. S. (2021). iSPLInception: an inception-ResNet deep learning architecture for human activity recognition. IEEE Access, 9, 68985-69001.
Gupta, S., & Saini, A. K. (2021). An artificial intelligence-based approach for managing risk of IT systems in adopting cloud. International Journal of Information Technology, 13(6), 2515-2523.
Gupta, S., & Saini, A. K. (2017). Cloud adoption: Linking business needs with system measures. Global Journal of Enterprise Information System, 9(2), 42-49.
Hayat, A., Morgado-Dias, F., Bhuyan, B. P., & Tomar, R. (2022). Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches. Information, 13(6), 275.
D. Nagpal and S. Gupta, "Human Activity Recognition and Prediction: Overview and Research Gaps," 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, 2023, pp. 1-5, doi: 10.1109/I2CT57861.2023.10126458.
Dua, N., Singh, S. N., Challa, S. K., Semwal, V. B., & Sai Kumar, M. L. S. (2023, January). A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data. In Machine Learning, Image Processing, Network Security and Data Sciences: 4th International Conference, MIND 2022, Virtual Event, January 19–20, 2023, Proceedings, Part I (pp. 52-71). Cham: Springer Nature Switzerland.
Ray, A., Kolekar, M. H., Balasubramanian, R., & Hafiane, A. (2023). Transfer Learning Enhanced Vision-based Human Activity Recognition: A Decade-long Analysis. International Journal of Information Management Data Insights, 3(1), 100142.
Nagpal, D., Kumar, R. (2023). Performance Analysis of Hybrid Deep Learning Approach for Human Actions Recognition. In: Ranganathan, G., Fernando, X., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 383. Springer, Singapore. https://doi.org/10.1007/978-981-19-4960-9_15.
Jegham, I., Khalifa, A. B., Alouani, I., & Mahjoub, M. A. (2020). Vision-based human action recognition: An overview and real world challenges. Forensic Science International: Digital Investigation, 32, 200901.
Khan, S., Khan, M. A., Alhaisoni, M., Tariq, U., Yong, H. S., Armghan, A., & Alenezi, F. (2021). Human action recognition: a paradigm of best deep learning features selection and serial based extended fusion. Sensors, 21(23), 7941.
D. Nagpal, S. Gupta, D. Kumar, Z. Illés, C. Verma and B. Dey, "goldenAGER: A Personalized Feature Fusion Activity Recognition Model for Elderly," in IEEE Access, doi: 10.1109/ACCESS.2023.3282439.
Chakraborty, S., Mondal, R., Singh, P. K., Sarkar, R., & Bhattacharjee, D. (2021). Transfer learning with fine tuning for human action recognition from still images. Multimedia Tools and Applications, 80, 20547-20578.
Wang, L., Zhou, Y., Li, R., & Ding, L. (2022). A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people. Knowledge-Based Systems, 252, 109351.
Malik, Z., & Shapiai, M. I. B. (2022). Human action interpretation using convolutional neural network: a survey. Machine Vision and Applications, 33(3), 37.
Aytar, Y., Transfer learning for object category detection. 2014, University of Oxford.
Su, Y.C., Chiu, T.H., Yeh, C.Y., Huang, H.F., Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network. arXiv preprint arXiv:1409.4127, 2014.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375.
Yao B, Jiang X, Khosla A, Lin AL, Guibas L, Fei-Fei L (2011) Human action recognition by learning bases of action attributes and parts. 2011 International Conference on Computer Vision, 1331–1338. https://doi.org/10.1109/ICCV.2011.6126386
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