Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Abstract

The quality of sleep may be a reflection of an elderly individual’s health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user’s sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user’s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users’ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

Keywords

sleep pattern / elder-care / pressure sensor / UWB tags / Naïve Bayes / Random Forest

References

[1] Foley D, Ancoli-Israel S, Britz P, Walsh J. Sleep disturbances and chronic disease in older adults: results of the 2003 national sleep foundation sleep in America survey. Journal of Psychosomatic Research, 2004, 56(5): 497−502

[2] Foley D, Monjan A A, Brown S L, Simonsick E M, Robert B W, Dan G B. Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep, 1995, 18(6): 425−432

[3] Cheng S P, Mei H. A personalized sleep quality assessment mechanism based on sleep pattern analysis. In: Proceedings of the 3rd IEEE International Conference on Innovations in Bio-Inspired Computing and Applications. 2012, 133−138

[4] Lutz W, Sanderson W, Scherbov S. The coming acceleration of global population ageing. Nature, 2008, 451(7179): 716−719

[5] Matt S. China’s big (and growing) problem with its elderly population. The Atlantic, 2013, 6

[6] Cheng C M, Hsu Y L, Young C M, Wu C H. Development of a portable device for tele-monitoring of snoring and OSAS symptoms. Telemedicine and e-HEALTH, 2008, 14(1): 55−68

[7] Zimmermann M, Toni I, de Lange Floris P. Body posture modulates action perception. The Journal of Neuroscience, 2013, 33(14): 5930−5938

[8] Vaughn M W, Boggs N, Letton A. Changes in sleep and wake in response to different sleeping surfaces: a pilot study. Applied Ergonomics, 2012, 43(2): 386−391

[9] Kambayashi Y, Hagiwara H. Estimating sleep cycle using body movement density. In: Proceedings of the 5th IEEE International Conference on Biomedical Engineering and Informatics. 2012, 1081−1085

[10] Bhadoria S S, Gupta H. A wearable personal healthcare and emergency information based on mobile application. International Journal of Scientific Research in Computer Science and Engineering, 2013, 1(04): 24−30

[11] Wang P C. Screening instruments of sleep disorders: actigraphy. Intelligent Systems, Control and Automation: Science and Engineering, 2012, 33−48

[12] Elbaz M, Yauy K, Metlaine A, Martoni M, Léger D. Validation of a new actigraph motion watch versus polysomnography on 70 healthy and suspected sleep-disordered subjects. Journal of Sleep Research, 2012, 21(111): 218

[13] Warren W T. Issues of validity in actigraphic sleep assessment. Sleep, 2004, 27(1): 158−165

[14] Terrill P I, Leong M, Barton K, Freakley C, Downey C, Vanniekerk M, Jorgensen G, Douglas J. Measuring leg movements during sleep using accelerometry: comparison with EMG and piezo-electric scored events. In: Proceedings of the 35th IEEE Annual International Conference on Engineering in Medicine and Biology Society. 2013, 6862−6865

[15] Cecilia O, Gaetano M. RFID technology for the neuroscience: feasibility of sleep disease monitoring. In: Proceedings of the 3rd IEEE European Conference on Antennas and Propagation. 2009, 2326−2330

[16] Paolo B. Position recognition to support bedsores prevention. IEEE Biomedical and Health Informatics, 2013, 17(1): 53−59

[17] Nikamalfard H, Zheng H, Wang H, Mulvenna M, McCullagh P, Jeffers P. A sleep pattern analysis and visualization system to support people with early dementia. In: Proceedings of the 5th IEEE International Conference on Pervasive Computing Technologies for Healthcare. 2011, 510−513

[18] Jukka K, Ilpo K, Pekka P. Long-term monitoring of movements in bed and their relation to subjective sleep quality. Sleep and Hypnosis, 2003, 5: 145−153

[19] Van der Loos H F M, Nino U, Hisato K. Development of sensate and robotic bed technologies for vital signs monitoring and sleep quality improvement. Autonomous Robots, 2003, 15(1): 67−79

[20] Huang W M, Wai A A P, Biswas J, Foo S F, Hsia C C, Liou K. Multimodal Situational Awareness for Eldercare. In: Proceedings of the 8th International Conference on Smart Homes and Health Telematics. 2010, 85−93

[21] Gaddam A, Kaur K, Gupta G S, Mukhopadhyay S C. Determination of sleep quality of inhabitant in a smart home using an intelligent bed sensing system. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference. 2010, 1613−1617

[22] Zhu X, Chen WX, Nemoto T, Kitamura K, Wei D M. Long-term monitoring of heart rate, respiration rhythm, and body movement during sleep based upon a network. Telemedicine and e-Health, 2010, 16(2): 244−253

[23] Witt J, Narbonneau F, Schukar M, Krebber K, De Jonckheere J, Jeanne M, Kinet D, Paquet B, Depré A, D’Angelo L T, Thiel T, Logier R. Medical textiles with embedded fiber optic sensors for monitoring of respiratory movement. Sensors Journal, 2012, 12(1): 246−254

[24] Tang Y W, Lin Y D, Lin Y T, Chakhap J. Using conductive fabric for capacitive EEG measurements. IETE Technical Review, 2013, 30(4): 295−302

[25] Hadiseh M, Juan R C, Giuseppe G, Miguel A G G, Xavier R. A wireless under-mattress sensor system for sleep monitoring in people with major depression. Biomedical Engineering, 2012, 9(1): 395

[26] Fu J B, Pan S L. UWB-over-fiber sensor network for accurate localization based on optical time-division multiplexing. In: Proceedings of the 12th IEEE International Conference on Optical Communications and Networks, 2013, 1−4

[27] Gezici S, Poor H V. Position estimation via ultra-wide-band signals. Proceedings of the IEEE. 2009, 97(2): 386−403

[28] Rovnakova J, Kocur D. UWB sensor based localization of persons with unknown motion activity. In: Proceedings of the 14th International Radar Symposium. 2013, 649−654

[29] Nijsure Y, Tay WP, Gunawan E, Wen F X, Yang Z, Guan Y L, Chua A P. An impulse radio ultra wideband system for contactless non-invasive respiratory monitoring. IEEE Transactions on Biomedical Engineering. 2013, 60(6): 1509−1517

[30] Simonceli L, Bittar R S M, Greters M E. Posture restrictions do not interfere in the results of canalith repostureing maneuver. Brazilian Journal of Otorhinolaryngology, 2005, 71: 55−59

[31] Pan J L. Medical applications of Ultra-WideBand (UWB). Survey Paper, 2007

[32] Duda R O, Hart P E, Stork D G. Pattern Classification. John Wiley & Sons, 2012

[33] Kohayi R. Scaling up the accuracy of naïve-bayes classifiers: a decision-tree hybrid. Knowledge Discovery in Databases, 1996: 202−207

[34] Breiman L. Random forests. Machine learning, 2001, 45(1): 5−32

[35] Kasiviswanathan K S, Avinash A. Radial basis function artificial neural network: spread selection. International Journal of Advanced Computer Science, 2012, 2(11)

[36] Hall M. Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning. 2000, 359−366

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