Exploring Activities of Daily Living Among the Elderly through Machine Learning Techniques
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Abstract
Activities of daily living (ADLs) is a term that is used to describe the activities performed in everyday life that involves the motion of the human body such as eating, walking, and sitting. ADLs can be used to determine the state of elderly people as a decline in ADL performance will generally mean a decline in the human body. It can act as an early indicator if an elderly person is experiencing underlying illness or health issue. This project aims to detect five different ADLs which are eating, cooking, sweeping, walking, and sitting and standing. A dataset was collected from twenty individuals performing each ADL at two different angles, a front view and a side view. A computer vision-based human pose estimation technique is used to extract the human body keypoints. These keypoint values are then processed and fit into multiple deep learning models for analysis. In this study, five different deep learning models namely LSTM, Bi-LSTM, CNN, RNN and Transformer models have been evaluated. The performance of each model is analysed and discussed. It was determined that the CNN model performed the best achieving a categorical accuracy of 82.86%.
Manuscript received: 14 Sep 2024 | Revised:4 Dec 2024 | Accepted: 11 Dec 2024 | Published: 31 Mar 2025
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References
J. He, H. Li and J. Tan, "Real-time Daily Activity Classification with Wireless Sensor Networks using Hidden Markov Model," Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3192-3195, 2007.
DOI: https://doi.org/10.1109/IEMBS.2007.4353008
A. Fleury, N. Noury and M. Vacher, "Supervised classification of activities of daily living in health smart homes using SVM," Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6099-6102, 2009.
DOI: https://doi.org/10.1109/IEMBS.2009.5334931
L. Gao, A.K. Bourke and J. Nelson, "A system for activity recognition using multi-sensor fusion," Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7869-7872, 2011.
DOI: https://doi.org/10.1109/IEMBS.2011.6091939
Q. Zhang, M. Karunanithi, D. Bradford and Y. van Kasteren, "Activity of Daily Living assessment through wireless sensor data," Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1752-1755, 2014.
DOI: https://doi.org/10.1109/EMBC.2014.6943947
C.H. Chen, K.B. Gan and N.A Abd Aziz, “Upper Limbs Extension and Flexion Angles Calculation and Visualization Using Two Wearable Inertial Measurement Units”, International Journal on Robotics, Automation and Sciences, Vol. 4, pp. 1–7, 2022.
DOI: https://doi.org/10.33093/ijoras.2022.4.1
G. Ercolano, D. Riccio and S. Rossi, "Two deep approaches for ADL recognition: A multi-scale LSTM and a CNN-LSTM with a 3D matrix skeleton representation," Proceedings of 26th IEEE International Symposium on Robot and Human Interactive Communication, pp. 877-882, 2017.
DOI: https://doi.org/10.1109/ROMAN.2017.8172406
R. Jurca, T. Cioara, I. Anghel, M. Antal, C. Pop and D. Moldovan, "Activities of Daily Living Classification using Recurrent Neural Networks," Proceedings of 17th RoEduNet Conference: Networking in Education and Research, pp. 1-4, 2018.
DOI: https://doi.org/10.1109/ROEDUNET.2018.8514124
P. Vanijkachorn and P. Visutsak, "A Deep Convolutional LSTM for ADLs Classification of the Elderly," Proceedings of International Conference on Data Analytics for Business and Industry, pp. 124-128, 2021. DOI: https://doi.org/10.1109/ICDABI53623.2021.9655856
S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors," Proceedings of 3rd International Conference on Big Data Analytics and Practices, pp. 21-26, 2022.
DOI: https://doi.org/10.1109/IBDAP55587.2022.9907460
S. Vyshali and S. Raja Mohamed, "Fall Detection & Daily Living Activity Recognition using CNN," Proceedings of International Conference on Inventive Computation Technologies, pp. 478-483, 2023.
DOI: https://doi.org/10.1109/ICICT57646.2023.10133971
H.S. Fang, J.F. Li, H.Y. Tang, C. Xu, H.Y. Zhu, Y.L. Xiu, Y.L. Li and C.W. Lu, "AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 6, pp. 7157-7173, 2023.
DOI: https://doi.org/10.1109/TPAMI.2022.3222784
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Proceedings of Neural Computation, Vol. 9, No. 8, pp. 1735–1780, 1997.
DOI: https://doi.org/10.1162/neco.1997.9.8.1735
L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie & L. Farhan, “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, Vol. 8, pp. 53, 2021.
DOI: https://doi.org/10.1186/s40537-021-00444-8
A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D: Nonlinear Phenomena, Vol. 404, pp. 0167-2789, 2020.
DOI: https://doi.org/10.1016/j.physd.2019.132306
T. Lin, Y. Wang, X. Liu and X. Qiu, “A survey of transformers,” AI Open, Vol. 3, pp. 111-132, 2022.
DOI: https://doi.org/10.1016/j.aiopen.2022.10.001
T.Y. Lin, et al, “Microsoft COCO: Common Objects in Context,” Computer Vision – ECCV 2014, Lecture Notes in Computer Science, Vol 8693. Springer, Cham, 2014.
DOI: https://doi.org/10.1007/978-3-319-10602-1_48
Z. Zhang, Z.W. Zou, P. Li, Y. Li, H. Su and G. Huang, “FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks,” arXiv preprints, pp. 05593, 2019.
DOI: https://doi.org/10.48550/arXiv.1908.05593
M. S. Z. Ahmad, N. A. Ab. Aziz, and A. K. Ghazali, "Development of automated attendance system using pretrained deep learning models," International Journal on Robotics, Automation and Sciences, vol. 6, no. 1, pp. 6–12, 2024. https://doi.org/10.33093/ijoras.2024.6.1.2
T. J. Jie and M. S. Sayeed, "Review on detecting pneumonia in deep learning," International Journal on Robotics, Automation and Sciences, vol. 6, no. 1, pp. 70–77, 2024.
DOI: https://doi.org/10.33093/ijoras.2024.6.1.10
H. Youns, S. Abbas, U. Hayat, M. H. Musaddiq, and A. Hashmi, "A cutting-edge hybrid approach for precise COVID-19 detection using deep learning," International Journal on Robotics, Automation and Sciences., vol. 6, no. 1, pp. 86–93, 2024.