Artificial Intelligence-Driven Smart Homecare: A Review of Applications, Challenges, and Prospects
DOI:
https://doi.org/10.5281/zenodo.17836245Keywords:
Smart Homecare, Artificial IntelligenceAbstract
With the accelerating global trend of population aging, traditional elderly care models are increasingly strained by limited service resources and growing quality demands. As a sustainable and cost-effective alternative, home-based elderly care has garnered widespread attention. However, older adults living at home often face critical challenges, including discontinuous health monitoring and insufficient emotional support. The rapid advancement of Artificial Intelligence (AI) technologies offers new pathways for developing intelligent elderly care solutions. This review systematically examines the major applications of AI in home-based care, focusing on two key domains: intelligent health monitoring and management and smart home environment systems. By analyzing representative research findings and practical implementations both domestically and internationally, this paper identifies core challenges across technical, user-centric, systemic, and ethical dimensions. Furthermore, it outlines future research directions, including personalized modeling, edge intelligence, empathetic human-AI interaction, multimodal data integration, and privacy-preserving strategies. This review aims to provide a theoretical foundation and a comprehensive framework to guide future research and practical advancements in AI-powered smart elderly care.
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Arora, H., & Sinha, R. (2025). Multi-Modal Graph Neural Networks for Physiological Signal Analysis. 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), 1202–1208. https://doi.org/10.1109/ICSADL65848.2025.10933022
Bačić, B., Feng, C., & Li, W. (2024). JY61 IMU SENSOR EXTERNAL VALIDITY: A FRAMEWORK FOR ADVANCED PEDOMETER ALGORITHM PERSONALISATION. ISBS Proceedings Archive, 42(1), 60.
Beard, J. R., Officer, A. M., & Cassels, A. K. (2016). The World Report on Ageing and Health. The Gerontologist, 56(Suppl_2), S163–S166. https://doi.org/10.1093/geront/gnw037
Chakraborty, A., Islam, M., Shahriyar, F., Islam, S., Zaman, H. U., & Hasan, M. (2023). Smart Home System: A Comprehensive Review. Journal of Electrical and Computer Engineering, 2023(1), 7616683. https://doi.org/10.1155/2023/7616683
Farsi, M. (2021). Application of ensemble RNN deep neural network to the fall detection through IoT environment. Alexandria Engineering Journal, 60(1), 199–211. https://doi.org/10.1016/j.aej.2020.06.056
Fei, X., Lu, J., Sun, Q., Feng, H., Wang, Y., Shi, W., Wang, A.-L., Tang, J., & Huang, C. (2025). Advancing Sequential Numerical Prediction in Autoregressive Models (No. arXiv:2505.13077). arXiv. https://doi.org/10.48550/arXiv.2505.13077
Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., & Alshemaimri, B. (2025). BERT applications in natural language processing: A review. Artificial Intelligence Review, 58(6), 166. https://doi.org/10.1007/s10462-025-11162-5
Jin, S., Wang, X., & Meng, Q. (2024). Spatial memory-augmented visual navigation based on hierarchical deep reinforcement learning in unknown environments. Knowledge-Based Systems, 285, 111358. https://doi.org/10.1016/j.knosys.2023.111358
Katmah, R., Shehhi, A. A., Jelinek, H. F., Hulleck, A. A., & Khalaf, K. (2023). A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4189–4202. https://doi.org/10.1109/TNSRE.2023.3325215
Li, B., Sun, B., Li, S., Chen, E., Liu, H., Weng, Y., Bai, Y., & Hu, M. (2024). Distinct but correct: Generating diversified and entity-revised medical response. Science China Information Sciences, 67(3), 132106. https://doi.org/10.1007/s11432-021-3534-9
Li, X., & Wang, Y. (2024). Deep Learning-Enhanced Adaptive Interface for Improved Accessibility in E-Government Platforms. 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), 1692–1695. https://doi.org/10.1109/ICFTIC64248.2024.10912984
Liu, X., Chau, K. Y., Zheng, J., Deng, D., & Tang, Y. M. (2024). Artificial intelligence approach for detecting and classifying abnormal behaviour in older adults using wearable sensors. Journal of Rehabilitation and Assistive Technologies Engineering, 11, 20556683241288459. https://doi.org/10.1177/20556683241288459
Liu, X., Hu, Q., Li, J., Li, W., Liu, T., Xin, M., & Jin, Q. (2024). Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series. Applied Energy, 367, 123368. https://doi.org/10.1016/j.apenergy.2024.123368
Liu, X., Wen, S., Liu, H., & Richard Yu, F. (2025). CPL-SLAM: Centralized Collaborative Multirobot Visual-Inertial SLAM Using Point-and-Line Features. IEEE Internet of Things Journal, 12(12), 21866–21875. https://doi.org/10.1109/JIOT.2025.3548958
Liu, Y., Qin, X., Gao, Y., Li, X., & Feng, C. (2025). SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition (No. arXiv:2505.19369). arXiv. https://doi.org/10.48550/arXiv.2505.19369
Lu, H., Niu, X., Wang, J., Wang, Y., Hu, Q., Tang, J., Zhang, Y., Yuan, K., Huang, B., Yu, Z., He, D., Deng, S., Chen, H., Chen, Y., & Shan, S. (2024). GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing. 322–331. https://openaccess.thecvf.com/content/CVPR2024W/CVPM/html/Lu_GPT_as_Psychologist___Preliminary_Evaluations_for_GPT-4V_on_CVPRW_2024_paper.html
Lu, H., Yu, Z., Niu, X., & Chen, Y.-C. (2023). Neuron Structure Modeling for Generalizable Remote Physiological Measurement. 18589–18599. https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Neuron_Structure_Modeling_for_Generalizable_Remote_Physiological_Measurement_CVPR_2023_paper.html
Mohan, D., Al-Hamid, D. Z., Chong, P. H. J., Sudheera, K. L. K., Gutierrez, J., Chan, H. C. B., & Li, H. (2024). Artificial Intelligence and IoT in Elderly Fall Prevention: A Review. IEEE Sensors Journal, 24(4), 4181–4198. https://doi.org/10.1109/JSEN.2023.3344605
Qian, K., Zhang, Z., Yamamoto, Y., & Schuller, B. W. (2021). Artificial Intelligence Internet of Things for the Elderly: From Assisted Living to Health-Care Monitoring. IEEE Signal Processing Magazine, 38(4), 78–88. https://doi.org/10.1109/MSP.2021.3057298
Qin, X. (2025). Saliency-Driven Multi-Scale Feature Discrepancy Fusion for Fine-Grained Video Anomaly Detection. INNO-PRESS: Journal of Emerging Applied AI, 1(1), Article 1. https://www.inno-press.com/index.php/JAAI/article/view/25
Saini, S. K., & Gupta, R. (2022). Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: State-of-the-art and future challenges. Artificial Intelligence Review, 55(2), 1519–1565. https://doi.org/10.1007/s10462-021-09999-7
Shi, J., Zhang, N., Wu, K., & Wang, Z. (2025). Application Status, Challenges, and Development Prospects of Smart Technologies in Home-Based Elder Care. Electronics, 14(12), Article 12. https://doi.org/10.3390/electronics14122463
Tang, J., Zhang, W., Liu, H., Yang, M., Jiang, B., Hu, G., & Bai, X. (2022). Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection. 4563–4572. https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.html
Wang, Y., Gong, C., Xu, Q., & Zheng, Y. (n.d.). Design of Privacy-Preserving Personalized Recommender System Based on Federated Learning.
Yang, T., Zhao, L., Li, W., Wu, J., & Zomaya, A. Y. (2021). Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach. Applied Energy, 300, 117335. https://doi.org/10.1016/j.apenergy.2021.117335
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Copyright (c) 2025 Yifan Gao, Xinyue Huang, Haoze Ni, Yixuan Dong, Chengwei Feng (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
