IoT and Deep Learning for Elderly Care: Monitoring and Predictive Health Management
Abstract
The aging population presents unique healthcare challenges, including the need for continuous monitoring and early intervention. This paper explores the application of IoT and deep learning for elderly care, focusing on monitoring and predictive health management. We present an IoT-based system that collects data from wearable sensors and home environment sensors, using deep learning algorithms to predict health issues such as falls, heart problems, and cognitive decline. A field study conducted in a senior living community demonstrates the system's ability to provide timely alerts and improve the overall quality of care. The paper also addresses the ethical considerations and data privacy concerns associated with elderly care technologies.
References
Smith, A., & Johnson, B. (2019). IoT-Based Smart Agriculture: Enhancing Crop Management and Yield Prediction. Journal of Agricultural and Food Information, 20(3), 215-230. https://doi.org/10.1080/10496505.2019.1598883
Kondru, V. L. P. (2014). Understanding Fluorosis: Implications for Dental and General Health. Transactions on Latest Trends in Health Sector, 6(6).
Kondru, V. L. P. (2014). Disparities in Dental Health Education: A Comparative Study of Rural and Urban Populations in India. International Journal of Medical Informatics and AI, 1(1), 1-17.
Kondru, V. L. P. (2014). A Review of the Association between Smoking, Alcohol Consumption, and Oral Cancer Risk. Journal of Healthcare AI and ML , 1(1), 1-18. https://journalpublication.wrcouncil.org/index.php/JHAM/article/view/6
Brown, C., & Davis, D. (2020). IoT Security: Emerging Threats and Countermeasures. Journal of Network and Computer Applications, 145, 102408. https://doi.org/10.1016/j.jnca.2019.102408
Yadav, H. (2023). Advancements in LoRaWAN Technology: Scalability and Energy Efficiency for IoT Applications. International Numeric Journal of Machine Learning and Robots, 7(7), 1-9.
Yadav, H. (2024). Scalable ETL pipelines for aggregating and manipulating IoT data for customer analytics and machine learning. International Journal of Creative Research In Computer Technology and Design, 6(6), 1-30.
Yadav, H. (2024). Anomaly detection using Machine Learning for temperature/humidity/leak detection IoT. International Transactions in Artificial Intelligence, 8(8), 1-18.
Lee, H., & Kim, J. (2018). Energy-Efficient Protocols for IoT Networks: A Survey. IEEE Communications Surveys & Tutorials, 20(3), 159-183. https://doi.org/10.1109/COMST.2018.2821559
Gonzalez, R., & Martinez, S. (2021). Blockchain for IoT Security and Privacy: A Systematic Review. Sensors, 21(10), 3264. https://doi.org/10.3390/s21103264
Patel, S., & Mehta, P. (2019). IoT in Healthcare: Applications, Challenges, and Future Trends. Health Informatics Journal, 25(2), 651-666. https://doi.org/10.1177/1460458217731250
Singh, A., & Gupta, R. (2020). Predictive Maintenance in Industrial IoT: Applications and Challenges. IEEE Transactions on Industrial Informatics, 16(8), 5344-5353. https://doi.org/10.1109/TII.2019.2963462
Chen, Q., & Zhang, L. (2019). Privacy-Preserving Data Analytics in IoT: Techniques and Applications. IEEE Internet of Things Journal, 6(2), 1504-1518. https://doi.org/10.1109/JIOT.2018.2871717
Wang, Z., & Zhao, Y. (2018). IoT-Enabled Smart Cities: A Comprehensive Review. Urban Computing and Smart Cities, 7(1), 29-45. https://doi.org/10.1109/UCSC.2018.8396258
Kumar, N., & Singh, P. (2021). Energy Harvesting Techniques for Sustainable IoT Devices. IEEE Transactions on Green Communications and Networking, 5(1), 201-214. https://doi.org/10.1109/TGCN.2020.3033445
Nadella, G. S., & Pillai, S. E. V. S. (2024, March). Examining the Indirect Impact of Information and System Quality on the Overall Educators' Use of E-Learning Tools: A PLS-SEM Analysis. In SoutheastCon 2024 (pp. 360-366). IEEE.
