2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 129-140, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. B. Sudhan 1 * , Deepak Kumar .A 2 , M. S. Minu 3 * , Mathan Kumar Mounagurusamy 4 , S. Navaneethan 5 , B. Venkataramanaiah 6
Doi: https://doi.org/10.54216/JISIoT.140110
Rapid urbanization needs major cities that change into smart cities to increase our lifestyle with respect to transportation, people, government, environmental sustainability, and more. In recent times, Internet of Things (IoT) and healthcare wearables have played a vital play in the progress of smart cities by providing enhanced healthcare services and an entire standard of living. Wearables offer real-time health records to individuals and healthcare providers, permitting for proactive management of chronic conditions and early recognition of health problems. While sleep is of major importance for a healthy life, it can be required to forecast sleep quality. Insufficient sleep affects mental health, physical, and emotional, and is a solution to many illnesses like heart disease, insulin resistance, stress, heart disease, and so on. Recently, deep learning (DL) techniques can be deployed to forecast the quality of sleep dependent upon the wearables data in the awake duration. Therefore, this paper presents an automated sleep quality recognition using hunger games search optimization with deep learning (ASQR-HGSODL) technique in the IoT-assisted smart healthcare system. The ASQR-HGSODL technique allows the IoT devices to perform a data collection process, which collects the data related to sleep activity. For the feature selection process, the ASQR-HGSODL technique applies an arithmetic optimization algorithm (AOA). For the prediction of sleep quality, the ASQR-HGSODL technique implements a convolutional long short-term memory (ConvLSTM) approach. Lastly, the HGSO technique has been applied for the optimum hyper parameter selection of the ConvLSTM approach. To exhibit the effectual prediction results of the ASQR-HGSODL approach, a range of simulation can be carried out. The investigational outputs highlight the improved outcome of the ASQR-HGSODL. technique with other DL methodologies.
Sleep quality prediction , Internet of Things , Deep learning , Hunger Games search algorithm , Wearables , Smart cities
[1] Zhang, L., Chen, S., Jin, X. and Wan, J., 2021. Smart Home Based Sleep Disorder Recognition for Ambient Assisted Living. In Artificial Intelligence and Security: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part II 7 (pp. 466-475). Springer International Publishing.
[2] Rajeswari, S.V.K.R. and Ponnusamy, V., 2022. Internet of Things and Artificial Intelligence in Biomedical Systems. In Artificial Intelligence for Innovative Healthcare Informatics (pp. 153-177). Cham: Springer International Publishing.
[3] Yang, F., Wu, Q., Hu, X., Ye, J., Yang, Y., Rao, H., Ma, R. and Hu, B., 2021. Internet-of-things-enabled data fusion method for sleep healthcare applications. IEEE Internet of Things Journal, 8(21), pp.15892-15905.
[4] Rangasamy, S., Rajamohan, K., Lavan, V.S., Mayur, C. and Lalitha, M.F., 2023. Evolutionized Industry With the Internet of Things. In Handbook of Research on Machine Learning-Enabled IoT for Smart Applications across Industries (pp. 407-434). IGI Global.
[5] Sun, Z., Kadry, S.N. and Krishnamoorthy, S., 2021. Internet of things-assisted advanced dynamic information processing system for physical education system. Technology and Health Care, 29(6), pp.1263-1275.
[6] Anu, V.M., Jagadeesh, M., Gladence, L.M., Srinivasulu, S., Revathy, S. and Rani, V.N., 2022. Tuning Hyper Parameters of Deep Learning Model to Monitor Obstructive Sleep Apnea (OSA). Sleep Epidemiology, 2, p.100031.
[7] Wang, Y., Hu, M., Zhou, Y., Li, Q., Yao, N., Zhai, G., Zhang, X.P. and Yang, X., 2020. Unobtrusive and automatic classification of multiple people’s abnormal respiratory patterns in real time using deep neural network and depth camera. IEEE Internet of Things Journal, 7(9), pp.8559-8571.
[8] Jaworski, D.J., Park, A. and Park, E.J., 2021. Internet of Things for sleep monitoring. IEEE Instrumentation & Measurement Magazine, 24(2), pp.30-36.
[9] Azman, N., Abd Ghani, M.K.B., Wicaksono, S.R., Kurniawan, B. and Repi, V.V.R., 2020. Insomnia analysis based on internet of things using electrocardiography and electromyography. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), pp.1406-1415.
[10] Sahu, K.S., Majowicz, S.E., Dubin, J.A. and Morita, P.P., 2021. NextGen public health surveillance and the internet of things (IoT). Frontiers in Public Health, 9, p.756675.
[11] Hamza, M.A., Abdalla Hashim, A.H., Alsolai, H., Gaddah, A., Othman, M., Yaseen, I., Rizwanullah, M. and Zamani, A.S., 2023. Wearables-Assisted Smart Health Monitoring for Sleep Quality Prediction Using Optimal Deep Learning. Sustainability, 15(2), p.1084.
[12] Yu, B., Wang, Y., Niu, K., Zeng, Y., Gu, T., Wang, L., Guan, C. and Zhang, D., 2021. WiFi-sleep: Sleep stage monitoring using commodity Wi-Fi devices. IEEE internet of things journal, 8(18), pp.13900-13913.
[13] Shen, Q., Yang, X., Zou, L., Wei, K., Wang, C. and Liu, G., 2022. Multitask residual shrinkage convolutional neural network for sleep apnea detection based on wearable bracelet photoplethysmography. IEEE Internet of Things Journal, 9(24), pp.25207-25222.
[14] Han, J., Men, A., Liu, Y., Yao, Z., Zhang, S., Yan, Y. and Chen, Q., 2023. IoT-V2E: an Uncertainty-Aware Cross-Modal Hashing Retrieval between Infrared-Videos and EEGs for Automated Sleep State Analysis. IEEE Internet of Things Journal.
[15] Brik, B., Esseghir, M., Merghem-Boulahia, L. and Snoussi, H., 2021. An IoT-based deep learning approach to analyse indoor thermal comfort of disabled people. Building and Environment, 203, p.108056.
[16] Nijaguna, G.S., Kumar, S., Devika, S.V., Lal, B. and Kumar, P., 2023, February. Internet of Things Based Tired Detection using Deep Learning Techniques. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-7). IEEE.
[17] Liu, M., Lin, Z., Xiao, P. and Xiang, W., 2022. Human biometric signals monitoring based on wifi channel state information using deep learning. arXiv preprint arXiv:2203.03980.
[18] Arora, A., Chakraborty, P., Bhatia, M.P.S. and Kumar, A., 2023. Deep‐SQA: A deep learning model using motor activity data for objective sleep quality assessment assisting digital wellness in healthcare 5.0. Expert Systems, p.e13321.
[19] Ghith, E.S. and Tolba, F.A.A., 2023. Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems. IEEE access, 11, pp.27138-27154.
[20] Jia, F., Tan, J., Lu, X. and Qian, J., 2023. Radar Timing Range–Doppler Spectral Target Detection Based on Attention ConvLSTM in Traffic Scenes. Remote Sensing, 15(17), p.4150.
[21] Zhan, B. and Gu, W., 2023. A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for solving complex optimization problems. IEEE Access.
[22] https://www.kaggle.com/code/jumpingmandt/sleepdata-study/data.
