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

doi:10.1007/s11704-015-4404-7

Front. Comput. Sci.

2015, Vol. 9

Issue (6) : 966-979 https://doi.org/10.1007/s11704-015-4404-7

RESEARCH ARTICLE

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

Hongbo NI^1,^*(

),Shu WU²,Bessam ABDULRAZAK³,Daqing ZHANG⁴,Xiaojuan MA⁵,Xingshe ZHOU¹

1. School of Computer Science, Northwestern Polytechnic University, Xi’an 710072, China
2. Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing 100090, China
3. Department of Computer, University of Sherbrooke, Sherbrooke JIK2R1, Canada
4. Handicom Lab, Institut Telecom SudParis, Evry 91011, France
5. Huawei Noah’s Ark Lab, Hong Kong, China

Download: PDF(571 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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

Corresponding Author(s): Hongbo NI

Just Accepted Date: 20 May 2015 Issue Date: 10 November 2015

Cite this article:

Shu WU,Bessam ABDULRAZAK,Daqing ZHANG, et al. Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment[J]. Front. Comput. Sci., 2015, 9(6): 966-979.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4404-7
https://academic.hep.com.cn/fcs/EN/Y2015/V9/I6/966

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 https://doi.org/10.1016/j.jpsychores.2004.02.010
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 https://doi.org/10.1109/ibica.2012.55
4	Lutz W, Sanderson W, Scherbov S. The coming acceleration of global population ageing. Nature, 2008, 451(7179): 716−719 https://doi.org/10.1038/nature06516
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 https://doi.org/10.1089/tmj.2007.0022
7	Zimmermann M, Toni I, de Lange Floris P. Body posture modulates action perception. The Journal of Neuroscience, 2013, 33(14): 5930−5938 https://doi.org/10.1523/JNEUROSCI.5570-12.2013
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 https://doi.org/10.1016/j.apergo.2011.06.012
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 https://doi.org/10.1109/bmei.2012.6513133
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 https://doi.org/10.1109/embc.2013.6611134
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 https://doi.org/10.1109/TITB.2012.2220374
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 https://doi.org/10.4108/icst.pervasivehealth.2011.245948
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 https://doi.org/10.1023/A:1024444917917
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 https://doi.org/10.1007/978-3-642-13778-5_11
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 https://doi.org/10.1109/imtc.2010.5488202
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 https://doi.org/10.1089/tmj.2009.0089
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 https://doi.org/10.1109/JSEN.2011.2158416
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 https://doi.org/10.4103/0256-4602.116720
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 https://doi.org/10.1109/icocn.2013.6617197
27	Gezici S, Poor H V. Position estimation via ultra-wide-band signals. Proceedings of the IEEE. 2009, 97(2): 386−403 https://doi.org/10.1109/JPROC.2008.2008840
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 https://doi.org/10.1109/TBME.2012.2237401
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 https://doi.org/10.1016/S1808-8694(15)31285-4
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 https://doi.org/10.1023/A:1010933404324
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

[1]

Supplementary Material-Highlights in 3-page ppt

Download

[1]	Qiang LV, Yixin CHEN, Zhaorong LI, Zhicheng CUI, Ling CHEN, Xing ZHANG, Haihua SHEN. Achieving data-driven actionability by combining learning and planning[J]. Front. Comput. Sci., 2018, 12(5): 939-949.
[2]	Liangxiao JIANG. Learning random forests for ranking[J]. Front Comput Sci Chin, 2011, 5(1): 79-86.

Viewed

Full text

Abstract

Cited

Shared

Discussed