Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Modeling Sleep Quality in the Elderly Using the Pittsburgh Sleep Quality Index Based on Non-Intrusive Smart Home Sensors

Document Type : Persian Original Article

Authors
Fatemeh Sadat Lesani 1
Mohammad Moballegh Naseri 2
1 Department of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran
2 Department of Electrical and Computer Engineering, Shahab Danesh University, Qom, Iran
10.22034/jscit.2025.521511.2121
Abstract
With the advancement of smart home technologies and their expanding applications in healthcare, monitoring and analyzing elderly sleep patterns has become a critical research area. Sleep, as a fundamental pillar of physical and mental health, requires continuous and accurate assessment. This study leverages data collected from non-intrusive environmental sensors within smart homes to investigate the influence of past sleep quality on current sleep status. A hybrid deep learning model combining CNN and LSTM architectures was employed to analyze these sleep patterns. Sleep quality was quantified using the Pittsburgh Sleep Quality Index (PSQI). Evaluation results demonstrate that the proposed model outperforms conventional approaches in predicting sleep quality. The Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for the CNN-LSTM hybrid model were 0.0006876, 0.02108, and 0.02622, respectively, indicating the model’s high effectiveness in recognizing elderly sleep behaviors. Data analysis further revealed that the combination of sleep efficiency and sleep interruptions had the greatest impact on predicting sleep quality. These findings suggest promising applications for intelligent health monitoring systems, especially in elderly care.
Keywords
Artificial Intelligence
Smart Home
Deep learning
Sleep Awake Behavioral Patterns
Pittsburgh Sleep Quality Index
[1] Roberts, A.W., S.U. Ogunwole, L. Blakeslee, and M.A. Rabe, The population 65 years and older in the United States: 2016. 2018: US Department of Commerce, Economics and Statistics Administration.
[2] Anderson, W.L. and J.M. Wiener, The impact of assistive technologies on formal and informal home care. The Gerontologist, 2015. 55(3): p. 422-433.
[3] Barnay, T. and S. Juin, Does home care for dependent elderly people improve their mental health? Journal of Health Economics, 2016. 45: p. 149-160.
[4] Zahraa Khais Shahid, S.S., Christer Åhlund, Unsupervised Forecasting and Anomaly Detection of ADLs in single-resident elderly smart homes. SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing, 2023.
[5] Demir, E., E. Köseoğlu, R. Sokullu, and B. Şeker, Smart home assistant for ambient assisted living of elderly people with dementia. Procedia computer science, 2017. 113: p. 609-614.
[6] Organization, W.H., Global action plan on physical activity 2018-2030: more active people for a healthier world. 2019: World Health Organization.
[7] Kentaro Matsui , T.Y., Kentaro Nagao , Tomohiro Utsumi , Ayumi Tsuru , Rei Otsuki , Naoko Ayabe , Megumi Hazumi , Masahiro Suzuki, Association of Subjective Quality and Quantity of Sleep with Quality of Life among a General Population. Int J Environ Res Public Health, 2021.
[8] Gao, J., Y. Zhang, Y. Chen, and T. Zhang, Unsupervised Human Activity Recognition Via Large Language Models and Iterative Evolution. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
[9] Hui Chen , C.G.-V., Kévin Bouchard ,Sébastien Gaboury ,Mélanie Couture ,Nathalie Bier , Sylvain Giroux, Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention  Sensors and Communications for the Social Good, 2024.
[10] D J Buysse , C.F.R., T H Monk, S R Berman, D J Kupfer, The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res, 1989.
[11] Morris, M., B. Adair, K. Miller, E. Ozanne, R. Hansen, A. Pearce, N. Santamaria, L. Viegas, M. Long, and C. Said, Smart-home technologies to assist older people to live well at home. Aging Sci, 2013. 1(1): p. 9p.
[12] Troiano, R.P., E. Stamatakis, and F.C. Bull, How can global physical activity surveillance adapt to evolving physical activity guidelines? Needs, challenges and future directions. British journal of sports medicine, 2020. 54(24): p. 1468-1473.
[13] Panel, C.C., N.F. Watson, M.S. Badr, G. Belenky, D.L. Bliwise, O.M. Buxton, D. Buysse, D.F. Dinges, J. Gangwisch, and M.A. Grandner, Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Journal of Clinical Sleep Medicine, 2015. 11(6): p. 591-592.
[14] Strain, T., K. Wijndaele, M. Pearce, and S. Brage, Considerations for the use of consumer-grade wearables and smartphones in population surveillance of physical activity. Journal for the Measurement of Physical Behaviour, 2022. 5(1): p. 8-14.
[15] Hyde, E.T., J.D. Omura, J.E. Fulton, A. Weldy, and S.A. Carlson, Physical activity surveillance using wearable activity monitors: are US adults willing to share their data? American Journal of Health Promotion, 2020. 34(6): p. 672-676.
[16] Fuller, D., E. Colwell, J. Low, K. Orychock, M.A. Tobin, B. Simango, R. Buote, D. Van Heerden, H. Luan, and K. Cullen, Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth and uHealth, 2020. 8(9): p. e18694.
[17] Straiton, N., M. Alharbi, A. Bauman, L. Neubeck, J. Gullick, R. Bhindi, and R. Gallagher, The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review. Maturitas, 2018. 112: p. 85-93.
[18] Scott, H., L. Lack, and N. Lovato, A systematic review of the accuracy of sleep wearable devices for estimating sleep onset. Sleep Medicine Reviews, 2020. 49: p. 101227.
[19] O’Driscoll, R., J. Turicchi, K. Beaulieu, S. Scott, J. Matu, K. Deighton, G. Finlayson, and J. Stubbs, How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies. British Journal of Sports Medicine, 2020. 54(6): p. 332-340.
[20] Evenson, K.R., M.M. Goto, and R.D. Furberg, Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 2015. 12(1): p. 1-22.
[21] Jennifer A Williams , D.J.C., Forecasting Behavior in Smart Homes Based on Sleep and Wake Patterns. Technol Health Care, 2017.
[22] Ronald Mutegeki, D.H., A CNN-LSTM Approach to Human Activity Recognition. Digital Signal Processing and Signal Processing Education Workshop, 2020.
[23] Lovell, K. and C. Liszewski, Summary: Normal Sleep Patterns and Sleep Disorders. Available from learn. chm. msu. edu/neuroed/neurobiology.(Accessed 6thJuly, 2014.), 2015.
[24] Jennifer A Williams , D.J.C., Forecasting Behavior in Smart  Homes Based on Sleep and Wake Patterns. Technol Health Care, 2017.
[25] Kasubi, M.D.H.J.W., Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques. SN Computer Science, 2021.
[26] Xiaoping Ma, N.G., Hwee Pink Tan, Identifying Elderly with Poor Sleep Quality using Unobtrusive In-home Sensors for Early Intervention. 2018.
[27] Xiangmao Chang, C.P., Guoliang Xing, Tian Hao, Gang Zhou, iSleep: A Smartphone System for Unobtrusive Sleep Quality Monitoring. ACM Transactions on Sensor Networks, 2020.
[28] Anshika Arora, P.C.M.P.S.B., Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning. Arabian Journal for Science and Engineering, 2020.
Journal of Soft Computing and Information Technology
Volume 14, Issue 3
Autumn - October
Autumn 2025
Pages 21-33