A Review of Recent Advances in Smart Homes for Improving Sleep Hygiene, and Sleep Quality
-
Shahram Samadi
,
Sepehr Samadi
,
Behnam Samadi
,
Mehrshad Samadi
,
Mehrdad Samadi
,
Mojtaba Shahbazi
Abstract
With the rising attention towards improving the quality of life and mental health, sleep hygiene and sleep quality have recently been the main topics of numerous studies. Quality of sleep not only affects our physical status but also plays a pivotal role in our psychological and emotional states. Sleep deprivation can increase the risk of cardiovascular and metabolic diseases along with the risk of impaired concentration and consequent road injury and accidents. As technology has become a main figure in our daily lives, technological advances have paid a great interest in improving the quality of sleep by enhancing the detection of sleep-related disorders and sleep abnormalities, particularly in the setting of smart homes and the Internet of Things (IoT). Smartphone applications, portable wearable gadgets, and devices along with more sophisticated and precise algorithms are now endeavoring to help us improve our quality of sleep and subsequently our quality of life. Hence, this review aims to illustrate a vivid picture of recent advancements in smart homes and their related technologies for improving sleep quality.
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2. Stickley A, Leinsalu M, DeVylder JE, Inoue Y, Koyanagi A. Sleep problems and depression among 237 023 community-dwelling adults in 46 low- and middle-income countries. Sci Rep 2019;9:12011.
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4. Yilmaz D, Tanrikulu F, Dikmen Y. Research on Sleep Quality and the Factors Affecting the Sleep Quality of the Nursing Students. Curr Health Sci J 2017;43:20-4.
5. Ferrie JE, Kumari M, Salo P, Singh-Manoux A, Kivimäki M. Sleep epidemiology—a rapidly growing field. Int J Epidemiol 2011;40:1431-7.
6. Turjamaa R, Pehkonen A, Kangasniemi M. How smart homes are used to support older people: An integrative review. Int J Older People Nurs 2019;14:e12260.
7. Amiribesheli M, Benmansour A, Bouchachia H. A review of smart homes in healthcare. J Ambient Intell Humaniz Comput 2015;6:495-517.
8. Leonidis A, Korozi M, Sykianaki E, Tsolakou E, Kouroumalis V, Ioannidi D, et al. Improving Stress Management and Sleep Hygiene in Intelligent Homes. Sensors (Basel) 2021;21:2398.
9. Biajar A, Mollayeva T, Sokoloff S, Colantonio A. Assistive technology to enable sleep function in patients with acquired brain injury: Issues and opportunities. Br J Occup Ther 2017;80:225-49.
10. Walia HK, Mehra R. Overview of Common Sleep Disorders and Intersection with Dermatologic Conditions. Int J Mol Sci 2016;17:654.
11. Sateia MJ. International classification of sleep disorders-third edition: highlights and modifications. Chest 2014;146:1387-94.
12. Surantha N, Kusuma Negara IGP, Isa SM. Internet of things for sleep quality monitoring system: A survey. 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS). 2016:1-6.
13. Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, et al. The future of sleep health: a data-driven revolution in sleep science and medicine. npj Digit Medic 2020;3:42.
14. Rundo JV, Downey R 3rd. Polysomnography. Handb Clin Neurol 2019;160:381-92.
15. Schwichtenberg A, Choe J, Kellerman A, Abel EA, Delp EJ. Pediatric videosomnography: can signal/video processing distinguish sleep and wake states? Front Pediatr 2018;6:158.
16. Tal A, Shinar Z, Shaki D, Codish S, Goldbart A. Validation of contact-free sleep monitoring device with comparison to polysomnography. J Clin Sleep Med 2017;13:517-22.
17. Paalasmaa J, Toivonen H, Partinen M. Adaptive heartbeat modeling for beat-to-beat heart rate measurement in ballistocardiograms. IEEE J Biomed Health Inform 2015;19:1945-52.
18. Yan R, Zhang C, Spruyt K, Wei L, Wang Z, Tian L, et al. Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomed Signal Process Control 2019;49:14-23.
19. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. 1995;3361:1995.
20. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-80.
21. Palotti J, Mall R, Aupetit M, Rueschman M, Singh M, Sathyanarayana A, et al. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit Med 2019;2:1-9.
22. Wortmann F, Flüchter K. Internet of things. Bus Inf Syst Eng 2015;57:221-4.
23. Zhang F, Wu C, Wang B, Wu M, Bugos D, Zhang H, et al. SMARS: Sleep Monitoring via Ambient Radio Signals. IEEE Trans Mob Comput 2021;20:217-31.
24. Sykianaki E, Leonidis A, Antona M, Stephanidis C. CaLmi: stress management in intelligent homes. The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM International Symposium; 2019:1202-5.
25. Tsolakou E, Leonidis A, Kouroumalis V, Korozi M, Antona M, Stephanidis C. HypnOS: A Sleep Monitoring and Recommendation System to Improve Sleep Hygiene in Intelligent Homes. In: Tsolakou E, Leonidis A, Kouroumalis V, Korozi M, Antona M, Stephanidis C, eds. Human Interaction, Emerging Technologies and Future Applications II. IHIET 2020; 2020:433-9.
26. Arnal PJ, Thorey V, Ballard ME, Hernandez AB, Guillot A, Jourde H, et al. The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging. Sleep 2020;43:zsaa097.
27. Lazazzera R, Laguna P, Gil E, Carrault G. Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove. Sensors (Basel) 2021;21:7976.
28. Concheiro-Moscoso P, Groba B, Martínez-Martínez FJ, Miranda-Duro MDC, Nieto-Riveiro L, Pousada T, et al. Use of the Xiaomi Mi Band for sleep monitoring and its influence on the daily life of older people living in a nursing home. Digit Health 2022;8:20552076221121162.
29. Brush AJ, Albrecht J, Miller R. Smart Homes. IEEE Pervasive Computing 2020;19:69-73.
30. Kiaghadi A, Homayounfar SZ, Gummeson J, Andrew T, Ganesan D. Phyjama: Physiological Sensing via Fiber-enhanced Pyjamas. Proc ACM Interact Mob Wearable Ubiquitous Technol 2019;3:89.
31. Liu C, Xiong J, Cai L, Feng L, Chen X, Fang D. Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 2019;3:96.
32. Ferrer-Lluis I, Castillo-Escario Y, Montserrat JM, Jané R. Sleeppos app: An automated smartphone application for angle based high resolution sleep position monitoring and treatment. Sensors 2021;21:4531.
33. Kalintha W, Kato T, Fukui Ki. SleepAge: Sleep Quality Assessment from Nocturnal Sounds in Home Environment. Procedia Comput Sci 2020;176:898-907.
34. Zhao X, Li J, Liu W, Zhang J, Li Y. Design of the sleeping aid system based on face recognition. Ad Hoc Networks 2020;99:102070.
35. Pan Q, Brulin D, Campo E. Home sleep monitoring based on wrist movement data processing. Procedia Comput Sci 2021;183:696-705.
36. Sonmezocak T, Kurt S. Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles. Biomed Signal Process Control 2021;69:102905.
37. Molin NL, Molin C, Dalpatadu RJ, Singh AK. Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings. Mach Learn Appl 2021;4:100022.
38. Zhang N, Li Y, Xiang S, Guo W, Zhang H, Tao C, et al. Imperceptible sleep monitoring bedding for remote sleep healthcare and early disease diagnosis. Nano Energy 2020;72:104664.
39. Goldstein C. Current and Future Roles of Consumer Sleep Technologies in Sleep Medicine. Sleep Med Clin 2020;15:391-408.
40. Han P, Li L, Zhang H, Guan L, Marques C, Savović S, et al. Low-cost plastic optical fiber sensor embedded in mattress for sleep performance monitoring. Opt Fiber Technol 2021;64:102541.
41. Yacchirema D, Sarabia-Jácome D, Palau CE, Esteve M. System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive Mob Comput 2018;50:25-40.
42. Li CZ, Borycki EM. Smart Homes for Healthcare. Stud Health Technol Inform 2019;257:283-7.
43. Houser KW, Esposito T. Human-Centric Lighting: Foundational Considerations and a Five-Step Design Process. Front Neurol 2021;12:630553.
44. Houser KW, Boyce PR, Zeitzer JM, Herf M. Human-centric lighting: Myth, magic or metaphor? Light Res Technol 2020;53:97-118.
45. Foster RG. Sleep, circadian rhythms, and health. Interface Focus 2020;10:20190098.
46. Schlangen LJ. CIE position statement on non-visual effects of light: recommending proper light at the proper time; 2019.
47. Shishegar N, Boubekri M, Stine-Morrow EAL, Rogers WA. Tuning environmental lighting improves objective and subjective sleep quality in older adults. Build Environ 2021;204:108096.
48. Engwall M, Fridh I, Johansson L, Bergbom I, Lindahl B. Lighting, sleep and circadian rhythm: An intervention study in the intensive care unit. Intensive Crit Care Nurs 2015;31:325-35.
49. Park KS, Choi SH. Smart technologies toward sleep monitoring at home. Biomed Eng Lett 2019;9:73-85.
50. Elliott JE, Tinsley CE, Reynolds C, Olson RJ, Weymann KB, Au-Yeung WM, et al. Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults. Sensors (Basel) 2022;22:5372.
2. Stickley A, Leinsalu M, DeVylder JE, Inoue Y, Koyanagi A. Sleep problems and depression among 237 023 community-dwelling adults in 46 low- and middle-income countries. Sci Rep 2019;9:12011.
3. Samadi S, Amirifard H, Eslami M, Khadembashiri MA, Khadembashiri MM, Najafi A. Evaluation of sleep quality and depressive symptoms among medical students during COVID-19 pandemic. Middle East Curr Psychiatry 2023;30:71.
4. Yilmaz D, Tanrikulu F, Dikmen Y. Research on Sleep Quality and the Factors Affecting the Sleep Quality of the Nursing Students. Curr Health Sci J 2017;43:20-4.
5. Ferrie JE, Kumari M, Salo P, Singh-Manoux A, Kivimäki M. Sleep epidemiology—a rapidly growing field. Int J Epidemiol 2011;40:1431-7.
6. Turjamaa R, Pehkonen A, Kangasniemi M. How smart homes are used to support older people: An integrative review. Int J Older People Nurs 2019;14:e12260.
7. Amiribesheli M, Benmansour A, Bouchachia H. A review of smart homes in healthcare. J Ambient Intell Humaniz Comput 2015;6:495-517.
8. Leonidis A, Korozi M, Sykianaki E, Tsolakou E, Kouroumalis V, Ioannidi D, et al. Improving Stress Management and Sleep Hygiene in Intelligent Homes. Sensors (Basel) 2021;21:2398.
9. Biajar A, Mollayeva T, Sokoloff S, Colantonio A. Assistive technology to enable sleep function in patients with acquired brain injury: Issues and opportunities. Br J Occup Ther 2017;80:225-49.
10. Walia HK, Mehra R. Overview of Common Sleep Disorders and Intersection with Dermatologic Conditions. Int J Mol Sci 2016;17:654.
11. Sateia MJ. International classification of sleep disorders-third edition: highlights and modifications. Chest 2014;146:1387-94.
12. Surantha N, Kusuma Negara IGP, Isa SM. Internet of things for sleep quality monitoring system: A survey. 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS). 2016:1-6.
13. Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, et al. The future of sleep health: a data-driven revolution in sleep science and medicine. npj Digit Medic 2020;3:42.
14. Rundo JV, Downey R 3rd. Polysomnography. Handb Clin Neurol 2019;160:381-92.
15. Schwichtenberg A, Choe J, Kellerman A, Abel EA, Delp EJ. Pediatric videosomnography: can signal/video processing distinguish sleep and wake states? Front Pediatr 2018;6:158.
16. Tal A, Shinar Z, Shaki D, Codish S, Goldbart A. Validation of contact-free sleep monitoring device with comparison to polysomnography. J Clin Sleep Med 2017;13:517-22.
17. Paalasmaa J, Toivonen H, Partinen M. Adaptive heartbeat modeling for beat-to-beat heart rate measurement in ballistocardiograms. IEEE J Biomed Health Inform 2015;19:1945-52.
18. Yan R, Zhang C, Spruyt K, Wei L, Wang Z, Tian L, et al. Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomed Signal Process Control 2019;49:14-23.
19. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. 1995;3361:1995.
20. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-80.
21. Palotti J, Mall R, Aupetit M, Rueschman M, Singh M, Sathyanarayana A, et al. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit Med 2019;2:1-9.
22. Wortmann F, Flüchter K. Internet of things. Bus Inf Syst Eng 2015;57:221-4.
23. Zhang F, Wu C, Wang B, Wu M, Bugos D, Zhang H, et al. SMARS: Sleep Monitoring via Ambient Radio Signals. IEEE Trans Mob Comput 2021;20:217-31.
24. Sykianaki E, Leonidis A, Antona M, Stephanidis C. CaLmi: stress management in intelligent homes. The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM International Symposium; 2019:1202-5.
25. Tsolakou E, Leonidis A, Kouroumalis V, Korozi M, Antona M, Stephanidis C. HypnOS: A Sleep Monitoring and Recommendation System to Improve Sleep Hygiene in Intelligent Homes. In: Tsolakou E, Leonidis A, Kouroumalis V, Korozi M, Antona M, Stephanidis C, eds. Human Interaction, Emerging Technologies and Future Applications II. IHIET 2020; 2020:433-9.
26. Arnal PJ, Thorey V, Ballard ME, Hernandez AB, Guillot A, Jourde H, et al. The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging. Sleep 2020;43:zsaa097.
27. Lazazzera R, Laguna P, Gil E, Carrault G. Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove. Sensors (Basel) 2021;21:7976.
28. Concheiro-Moscoso P, Groba B, Martínez-Martínez FJ, Miranda-Duro MDC, Nieto-Riveiro L, Pousada T, et al. Use of the Xiaomi Mi Band for sleep monitoring and its influence on the daily life of older people living in a nursing home. Digit Health 2022;8:20552076221121162.
29. Brush AJ, Albrecht J, Miller R. Smart Homes. IEEE Pervasive Computing 2020;19:69-73.
30. Kiaghadi A, Homayounfar SZ, Gummeson J, Andrew T, Ganesan D. Phyjama: Physiological Sensing via Fiber-enhanced Pyjamas. Proc ACM Interact Mob Wearable Ubiquitous Technol 2019;3:89.
31. Liu C, Xiong J, Cai L, Feng L, Chen X, Fang D. Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 2019;3:96.
32. Ferrer-Lluis I, Castillo-Escario Y, Montserrat JM, Jané R. Sleeppos app: An automated smartphone application for angle based high resolution sleep position monitoring and treatment. Sensors 2021;21:4531.
33. Kalintha W, Kato T, Fukui Ki. SleepAge: Sleep Quality Assessment from Nocturnal Sounds in Home Environment. Procedia Comput Sci 2020;176:898-907.
34. Zhao X, Li J, Liu W, Zhang J, Li Y. Design of the sleeping aid system based on face recognition. Ad Hoc Networks 2020;99:102070.
35. Pan Q, Brulin D, Campo E. Home sleep monitoring based on wrist movement data processing. Procedia Comput Sci 2021;183:696-705.
36. Sonmezocak T, Kurt S. Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles. Biomed Signal Process Control 2021;69:102905.
37. Molin NL, Molin C, Dalpatadu RJ, Singh AK. Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings. Mach Learn Appl 2021;4:100022.
38. Zhang N, Li Y, Xiang S, Guo W, Zhang H, Tao C, et al. Imperceptible sleep monitoring bedding for remote sleep healthcare and early disease diagnosis. Nano Energy 2020;72:104664.
39. Goldstein C. Current and Future Roles of Consumer Sleep Technologies in Sleep Medicine. Sleep Med Clin 2020;15:391-408.
40. Han P, Li L, Zhang H, Guan L, Marques C, Savović S, et al. Low-cost plastic optical fiber sensor embedded in mattress for sleep performance monitoring. Opt Fiber Technol 2021;64:102541.
41. Yacchirema D, Sarabia-Jácome D, Palau CE, Esteve M. System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive Mob Comput 2018;50:25-40.
42. Li CZ, Borycki EM. Smart Homes for Healthcare. Stud Health Technol Inform 2019;257:283-7.
43. Houser KW, Esposito T. Human-Centric Lighting: Foundational Considerations and a Five-Step Design Process. Front Neurol 2021;12:630553.
44. Houser KW, Boyce PR, Zeitzer JM, Herf M. Human-centric lighting: Myth, magic or metaphor? Light Res Technol 2020;53:97-118.
45. Foster RG. Sleep, circadian rhythms, and health. Interface Focus 2020;10:20190098.
46. Schlangen LJ. CIE position statement on non-visual effects of light: recommending proper light at the proper time; 2019.
47. Shishegar N, Boubekri M, Stine-Morrow EAL, Rogers WA. Tuning environmental lighting improves objective and subjective sleep quality in older adults. Build Environ 2021;204:108096.
48. Engwall M, Fridh I, Johansson L, Bergbom I, Lindahl B. Lighting, sleep and circadian rhythm: An intervention study in the intensive care unit. Intensive Crit Care Nurs 2015;31:325-35.
49. Park KS, Choi SH. Smart technologies toward sleep monitoring at home. Biomed Eng Lett 2019;9:73-85.
50. Elliott JE, Tinsley CE, Reynolds C, Olson RJ, Weymann KB, Au-Yeung WM, et al. Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults. Sensors (Basel) 2022;22:5372.
| Files | ||
| Issue | Vol 61 No 9 (2023) | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/acta.v61i9.15279 | |
| Keywords | ||
| Sleep Smart home Sleep hygiene Sleep quality Internet of things (IoT) | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Samadi S, Samadi S, Samadi B, Samadi M, Samadi M, Shahbazi M. A Review of Recent Advances in Smart Homes for Improving Sleep Hygiene, and Sleep Quality. Acta Med Iran. 2024;61(9):499-507.
