Sleep Monitoring Technology: Evolution, Current Landscape, and Future Horizons
DOI:
https://doi.org/10.54691/y4ej6z93Keywords:
Sleep Monitoring, Polysomnography, Wearable Sensors, Actigraphy, Ballistocardiography, Artificial Intelligence, Elderly Care, Sensor Fusion.Abstract
Sleep is a fundamental pillar of human health, intricately linked to cognitive function, cardiovascular well-being, and overall quality of life. However, sleep disorders are increasingly prevalent, posing significant public health challenges. Effective management begins with accurate and accessible monitoring. This review comprehensively examines the technological evolution of sleep monitoring, from the clinical gold standard to emerging home-based and wearable solutions. We detail the principles, sensors, and parameters involved, contrasting polysomnography (PSG) with alternatives like actigraphy, ballistocardiography (BCG), and novel sensor-based systems. The discussion extends to multidimensional sleep parameter recognition techniques leveraging artificial intelligence (AI). Furthermore, we analyze the specific context and technological preferences for sleep monitoring in older adults. Finally, we explore future trajectories, including the integration of multi-sensor fusion, advanced materials like graphdiyne for ultra-sensitive sensing, AI-driven edge computing, and innovative intervention methods such as microneedle-based systems. This synthesis aims to provide a holistic view of the field, highlighting technological convergence as the key to personalized, precise, and preventive sleep healthcare.
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[1] P.K. Stein and Y. Pu: Heart rate variability, sleep and sleep disorders, Sleep Med. Rev., Vol. 16 (2012) No. 1, p. 47-66.
[2] D. Léger, E. Debellemaniere, A. Rabat, V. Bayon, K. Benchenane, and M. Chennaoui: Slow-wave sleep: From the cell to the clinic, Sleep Med. Rev., Vol. 41 (2018), p. 113-132.
[3] N. Litlenales: Sleep Revolution (Guiyang, Guizhou: Guizhou Sci. Technol. Press 2020).
[4] H.Lamberts: Incidentie en prevalentie van gezondheidsproblemen in de huisartspraktijk, Huisarts en wetenschap, Vol.25 (1982) No.40, p. 1-4.
[5] X. X. Liu, Z. Wang, S.J. Chen, et al: Sleep health in China: status, challenges, and promotion strategies, The Lancet Public Health, Vol.10 (2025) No.12, p.e1055-e1065.
[6] S.A. Landry, C. Beatty, L.D. Thomson, A.M. Wong, B.A. Edwards, G.S. Hamilton, and S.A. Joosten: A review of supine position related obstructive sleep apnea: Classification, epidemiology, pathogenesis and treatment, Sleep Med. Rev., Vol. 72 (2023), p. 101847.
[7] Singh, R. K. Tripathy, and R. B. Pachori: Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis, Digit. Signal Process., Vol. 104 (2020), p. 102796.
[8] H. Hong, L. Zhang, H. Zhao, et al: Microwave sensing and sleep: Noncontact sleep-monitoring technology with microwave biomedical radar, IEEE Microw. Mag., Vol. 20 (2019) No. 8, p. 18-29.
[9] C. He, X. Wang, Y. Wen, et al: Sleep Monitoring and Sleep-Aid Intervention Methods: A Review, IEEE Internet Things J., Vol. 12 (2025) No. 10, p. 32776-32789.
[10] S. M. Riedy, M. G. Smith, S. Rocha, and M. Basner: Noise as a sleep aid: A systematic review, Sleep Med. Rev., Vol. 55 (2021) No. 101385.
[11] S.N. Ghazi, A. Behrens, J. Berner, J. Sanmartin Berglund, and P. Anderberg: Objective sleep monitoring at home in older adults: A scoping review, Journal of Sleep Research, Vol.34 (2025) No.4, p. e14436.
[12] S. Roombham, D. Lovell, J. Cheung, and D. Perrin: Promises and challenges in the use of consumer-grade devices for sleep monitoring, IEEE Rev. Biomed. Eng., Vol. 11 (2018), p. 53-67.
[13] T. Nakamura, Y. D. Alqurashi, M. J. Morrell, and D. P. Mandic: Hearables: Automatic overnight sleep monitoring with standardized in-ear EEG sensor, IEEE Trans. Biomed. Eng., Vol. 67 (2020) No. 1, p. 203-212.
[14] P. Menara and F. Faradji: A novel multi-class EEG-based sleep stage classification system, IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 26 (2018) No. 1, p. 84-95.
[15] J.T. Schwabedal, M. Riedl, T. Penzel, and N. Wessel: Alpha-wave frequency characteristics in health and insomnia during sleep, J. Sleep Res., Vol. 25 (2016) No. 3, p. 278-286.
[16] R.M. Kwasnicki, G.W.V. Cross, L. Geoghegan, et al: A lightweight sensing platform for monitoring sleep quality and posture: A simulated validation study, Eur. J. Med. Res., Vol. 23 (2018) No. 1, p. 1-9.
[17] F. Deng, J. Dong, X. Wang, et al: Design and implementation of a noncontact sleep monitoring system using infrared cameras and motion sensor, IEEE Trans. Instrum. Meas., Vol. 67 (2018) No. 7, p. 1555-1563.
[18] F. Portier, A. Portmann, P. Czernichow, et al: Evaluation of home versus laboratory polysomnography in the diagnosis of sleep apnea syndrome, Am J Respir Crit Care Med., (2000).
[19] B. Lechat, G. Naik, A. Reynolds, et al: Multinight prevalence, variability, and diagnostic misclassification of obstructive sleep apnea, Am. J. Respir. Crit. Care Med., Vol. 205 (2022) No. 5, p. 563-569.
[20] H. Scott, G. Naik, B. Lechat, et al: Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data, Sleep Health, Vol. 10 (2024) No. 1, p. 91-97.
[21] B. Lechat, H. Scott, J. Manners, et al: Multinight measurement for diagnosis and simplified monitoring of obstructive sleep apnea, Sleep Med. Rev., Vol. 72 (2023), p. 101843.
[22] American Academy of Sleep Medicine Task Force: Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research, Sleep, (1999).
[23] M. Bruyneel, C. Sanida, G. Art, et al: Sleep efficiency during sleep studies: Results of a prospective study comparing home-based and in-hospital polysomnography, J. Sleep Res., Vol. 20 (2011) No. 1pt2, p. 201-206.
[24] Y. K. Wang, H. Y. Chen, and J. R. Chen: Unobtrusive sleep monitoring using movement activity by video analysis, Electronics, Vol. 8 (2019) No. 7, p. 812.
[25] P. Jakkaew and T. Onoye: Non-contact respiration monitoring and body movements detection for sleep using thermal imaging, Sensors, Vol. 20 (2020) No. 21, p. 6307.
[26] S. M. Mohammadi, S. Enshaeifar, A. Hilton, D. J. Dijk, and K. Wells: Transfer learning for clinical sleep pose detection using a single 2-D IR camera, IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 29 (2020), p. 290-299.
[27] Z. Chen and Y. Wang: Remote recognition of in-bed postures using a thermopile array sensor with machine learning, IEEE Sensors J., Vol. 21 (2021) No. 9, p. 10428-10436.
[28] F. Wang, X. Zeng, C. Wu, B. Wang, and K. J. R. Liu: mmHRV: Contactless heart rate variability monitoring using millimeter-wave radio, IEEE Internet Things J., Vol. 8 (2021) No. 22, p. 16623-16636.
[29] M. Hur, K. Han, and S. Hong: Multiple human heart rate variability detection using MIMO FMCW radar with differential beam techniques, IEEE Trans. Radar Syst., Vol. 1 (2023), p. 698-706.
[30] S. Ahmed, Y. Lee, Y.H. Lim, S,H. Cho, H.K. Park, and S.H. Cho: Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar, Sci. Rep., Vol. 12 (2022) No. 1.
[31] Y. D’Mello, J. Skoric, S. Xu, P.J. Roche, M. Lortie, S. Gagnon, and D.V. Plant: Real-time cardiac beat detection and heart rate monitoring from time seismocardiography and gyrocardiography, Sensors, Vol. 19 (2019) No. 16, p. 3472.
[32] M. Baboli, A. Singh, B. Soll, O. Boric-Lubecke, and V. M. Lubecke: Wireless sleep apnea detection using continuous wave quadrature doppler radar, IEEE Sensors J., Vol. 20 (2020) No. 1, p. 538-545.
[33] H. Yoon, S. H. Hwang, J.-W. Choi, Y. J. Lee, D.-U. Jeong, and K. S. Park: Slow-wave sleep estimation for healthy subjects and OSA patients using R-R Intervals, IEEE J. Biomed. Health Informat., Vol. 22 (2018) No. 1, p. 119-128.
[34] Y. Gu, Y. Zhang, J. Li, Y. Ji, X. An, and F. Ren: Sleepy: Wireless channel data driven sleep monitoring via commodity WiFi devices, IEEE Trans. Big Data, Vol. 6 (2020) No. 2, p. 258-268.
[35] B. Yu, Y. Wang, K. Niu, et al: WiFi-sleep: Sleep stage monitoring using commodity Wi-Fi devices, IEEE Internet Things J., Vol. 8 (2021) No. 18, p. 13900-13913.
[36] M. N. Markandeya, U. R. Abeyratne, and C. Hukins: Overnight airway obstruction severity prediction centered on acoustic properties of smart phone: validation with esophageal pressure, Physiol. Meas., Vol. 41 (2020) No. 10.
[37] J. M. Perez-Mateos, M. Tenhunen, A. Varri, S. L. Himanen, and J. Vilk: Detection of snores using source separation on an emfit signal, IEEE J. Biomed. Health Inform., Vol. 22 (2018) No. 4, p. 1157-1167.
[38] W.R. Pigeon, M. Taylor, A. Bui, C. Oleynk, P. Walsh, and T.M. Bishop: Validation of the sleep-wake scoring of a new wrist-worn sleep monitoring device, J. Clin. Sleep Med., Vol. 14 (2018) No. 6, p. 1057-1062.
[39] C. Kuo, Y. Liu, D. Chang, C. Young, F. Shaw, and S. Liang: Development and evaluation of a wearable device for sleep quality assessment, IEEE Trans. Biomed. Eng., Vol. 64 (2017) No. 7, p. 1547-1557.
[40] S. Nauman Ghazi, A. Behrens, J. Berner, J. S. Berglund, P. Anderberg: Objective sleep monitoring at home in older adults: A scoping review, J Sleep Res., (2024).
[41] J.L. Ong, H.A. Golkashani, S. Ghorbani, K.F. Wong, N.I. Chee, A.R. Willoughby, and M.W. Chee: Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices, Sleep Health, (2024).
[42] A.J. Boe, L.L. McGee Koch, M.K. O’Brien, et al: Automating sleep stage classification using wireless wearable sensors, NPJ Digit. Med., Vol. 2 (2019) No. 1, p. 131.
[43] L. Zhang, H. Wu, X. Zhang, X. Wei, F. Hou, and Y. Ma: Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes, Sleep Med., Vol. 67 (2020), p. 217-224.
[44] M. Szypulska, Z. Piotrowski: Prediction of fatigue and sleep onset using HRV analysis, Proceedings of the 19th International Conference Mixed Design of Integrated Circuits and Systems-MIXDES 2012. IEEE, (2012), p. 543-546.
[45] M. Jafari Tadi, E. Lehtonen, A. Saraste, et al: Gyrocardiography: A new non-invasive monitoring method for the assessment of cardiac mechanics and the estimation of hemodynamic variables, Sci. Rep., Vol. 7 (2017) No. 1, p. 6823.
[46] P. Daux, E. Strumban, and R. G. Maev: Wearable device for increasing the slow wave sleep stage by electrocutaneous stimulation, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, (2017), p. 1-5.
[47] X. Wang, M. Cheng, Y. Wang, S. Liu, Z. Tian, F. Jiang, and H. Zhang: Obstructive sleep apnea detection using eeg-sensor with convolutional neural networks, Multimedia Tools Appl., Vol. 79 (2020) No. 23, p. 15813-15827.
[48] P.J. Arnal, V. Thorey, E. Debellemaniere, et al: The dream headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging, Sleep, Vol. 43 (2020) No. 11.
[49] M. Mohamed, N. Mohamed, and J. G. Kim: Advancements in wearable EEG technology for improved home-based sleep monitoring and assessment: A review, Biosensors, Vol. 13 (2023) No. 12, p. 1019.
[50] G. Matar, G. Kaddoum, J. Carrier, and J. M Lina: Kalman filtering for posture-adaptive in-bed breathing rate monitoring using bed-sheet pressure sensors, IEEE Sensors J., Vol. 21 (2021) No. 13, p. 14339-14351.
[51] L. Peng, Z. Yin, W. Song, W. Yao, H. Ren, and L. Yang: Sleep monitoring with hidden Markov model for physical conditions tracking, IEEE Sensors J., Vol. 21 (2021) No. 13, p. 14232-14239.
[52] Y. Chao, T. Liu, and L. M. Shen: Method of recognizing sleep postures based on air pressure sensor and convolutional neural network: For an air spring mattress, Eng. Appl. Artif. Intell., Vol. 121 (2023).
[53] S. Rajala and J. Lekkala: Film-type sensor materials PVDF and EMFI in measurement of cardiorespiratory signals—A review, IEEE Sensors J., Vol. 12 (2012) No. 3, p. 439-446.
[54] W. Wang, Z. Pang, L. Peng, et al: Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor, J. Eng. Fibers Fabrics, Vol. 15 (2020), p. 1-11.
[55] Y. Li, B. Dong, Y. Zhao, E. Chen, X. Wang, W. Zhao, and Y. Wang: Smart optic fiber mattress for animal sleep continuous monitoring based multi-modal interferometry, J. Lightw. Technol., Vol. 39 (2021) No. 12, p. 4131-4147.
[56] W. Chen, Y. Zhang, H. Yang, Y. Qiu, H. Li, and Z. Chen: Non-invasive measurement of vital signs based on seven-core fiber interferometer, IEEE Sensors J., Vol. 21 (2021) No. 9, p. 10703-10710.
[57] J. Y. Kim, C. H. Chu, and M. S. Kang: Drift-based unobtrusive sensing for sleep quality monitoring and assessment, IEEE Sensors J., Vol. 21 (2021) No. 3, p. 3799-3809.
[58] F. Li, M. Valero, J. Clemente, Z. Tse, and W. Song: Smart sleep monitoring system via passively sensing human vibration signals, IEEE Sensors J., Vol. 21 (2021) No. 13, p. 14466-14473.
[59] S. Morra, A. Hossein, D. Gorlier, J. Rabineau, M. Chaumont, P.F. Migeotte, and P. Van De Borne: Ballistocardiography and seismocardiography detection of hemodynamic changes during simulated obstructive apnea, Physiol. Meas., Vol. 41 (2020) No. 6.
[60] S. Vandana, T. Palla, S. Pallempati, and V. Padavala: Smart pillow, Int. J. Anal. Exp. Modal Anal., Vol. 12 (2020) No. 8, p. 2172-2178.
[61] D. Mahanta, H. Bordoloi, and S. J. Saikia: LabVIEW based smart pillow, 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, (2020), p. 632-636.
[62] Z. Xu, J. Wang, Q. Yu, J. Liu, X. Ye, J. Xu, and W. Xu: Amino-modified graphdiyne-based flexible respiratory sensor for monitoring sleep apnea syndrome, Sci China Mater, Vol. 68 (2025) No. 12, p. 4384-4391.
[63] S. S. Upadhyay, A. N. Cheeran, and J. H. Nirmal: Thomson multitaper MFCC and PLP voice features for early detection of Parkinson disease, Biomed. Signal Process. Control, Vol. 46 (2018), p. 293-301.
[64] S. P. Jin, X. F. Wang, L. L. Du, and D. He: Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN, Appl. Acoust., Vol. 172 (2021).
[65] R. K. Tripathy and A. U. Rajendra: Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework, Biocybern. Biomed. Eng., Vol. 38 (2018) No. 4, p. 890-902.
[66] A. R. Hassan and A. Subasi: A decision support system for automated identification of sleep stages from single-channel EEG signals, Knowl.-Based Syst., Vol. 128 (2017), p. 115-124.
[67] N. Ghassemi, R. Boostani, and S. Sanei: SleepFCN: A fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms, IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 30 (2022), p. 2088-2096.
[68] J. Xie, X. Aubert, X. Long, J. van Dijk, B. Arsenali, P. Fonseca, and S. Overeem: Audio-based snore detection using deep neural networks, Comput. Methods Progr. Biomed., Vol. 200 (2021).
[69] Z. Chao, W. Cui, and J. Guo: MSSC-BiMamba: Multimodal sleep stage classification and early diagnosis of sleep disorders with bidirectional mamba, arXiv:2406.20142, (2024).
[70] X. Zhou, Y. Han, Z. Chen, C. Liu, Y. Ding, Z. Jia, and Y. Liu: Bi-MAMsleep: Bidirectional temporal mamba for EEG sleep staging, arXiv:2411.01589, (2024).
[71] H. E. Romero, N. Ma, and G. J. Brown: Snorer diarisation based on deep neural network embeddings, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, (2020), p. 876-880.
[72] M. Soltanian and K. Borna: COVID-19 recognition from cough sounds using lightweight separable-quadratic convolutional network, Biomed. Signal Process. Control, Vol. 72 (2022) No. 1.
[73] J. Liu, H. Wang, T. Liu, et al: Multimodal hydrogel-based respiratory monitoring system for diagnosing obstructive sleep apnea syndrome, Adv Funct Mater, Vol. 32 (2022), p. 2246866.
[74] Y. Bai, L. Wang, X. Zou, et al: Atomic sulfur-bonded titanium carbide nanosheets for flexible piezoresistive sensor in monitoring sleep apnea syndrome, Matter, Vol. 8 (2025), p. 101927.
[75] S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh, and W.-C. Hong: Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward, IEEE Access, Vol. 8 (2020), p. 474-488.
[76] H. A. Rashid, A. N. Mazumder, U. P. K. Niyogi, and T. Mohsenin: CoughNet: A flexible low power CNN-LSTM processor for cough sound detection, 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, (2021), p. 1-4.
[77] N. Perteklis and G. K. Adam: Cough sound classification based on similarity metrics, 2021 44th International Conference on Telecommunications and Signal Processing (TSP). IEEE, (2021), p. 214-217.
[78] J. R. D. Espiritu: Aging-related sleep changes, Clin. Geriatr. Med., Vol. 24 (2008) No. 1, p. 1-14.
[79] M. Altendahl, D.L. Cotter, A.M. Staffaroni, et al: REM sleep is associated with white matter integrity in cognitively healthy, older adults, PLoS One, Vol. 15 (2020), p. e0235395.
[80] A.S. Lim, M. Kowgier, L. Yu, A.S. Buchman, and D.A. Bennett: Sleep fragmentation and the risk of incident Alzheimer's disease and cognitive decline in older persons, Sleep, Vol. 36 (2013) No. 7, p. 1027-1032.
[81] N. Yunyoung, K. Yescock, and L. Jinseok: Sleep monitoring based on a tri-axial accelerometer and a pressure sensor, Sensors, (2020).
[82] X. Li, Y. Gong, X. Jin, and P. Shang: Sleep posture recognition based on machine learning: A systematic review, Pervasive Mobile Comput., Vol. 90 (2023).
[83] J. de, C. J. Burger, P. van, E. S. Hermanides, J. Nanayakkara, P. Gemke, R. Rutters, F. Stenvers, and D. J.: Sleep assessment using EEG-based wearables-a systematic review, Sleep Med. Rev., Vol. 76 (2024), p. 101951.
[84] T. Li, J. Li, Z. Wang, et al: A dissolvable microneedle patch based on medical adhesive tape for transdermal drug delivery, 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS). IEEE, (Gainesville, FL, USA), (2021), p. 18-21.
[85] C. He, Z. Fang, H. Wu, X. Li, L. Cheng, Y. Wen, and J. Lin: A flexible and dissolving traditional Chinese medicine microneedle patch for sleep-aid intervention, Heliyon, Vol. 10 (2024) No. 12.
[86] J. Kim, S. Kim, W. J. Lee, J. R. Kim, S. I. Nam, and C. H. Yun: Effects of at-home transcutaneous electrical trigeminal nerve stimulation on sleep quality in patients with insomnia, Sleep Med., Vol. 115 (2024), p. 173.
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