[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A novel depth image analysis for sleep posture estimation

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Recognition of sleep posture and its changes are related to information monitoring in a number of health-related applications such as apnea prevention and elderly care. This paper uses a less privacy-invading approach to classify sleep postures of a person in various configurations including side and supine postures. In order to accomplish this, a single depth sensor has been utilized to collect selective depth signals and populated a dataset associated with the depth data. The data is then analyzed by a novel frequency-based feature selection approach. These extracted features were then correlated in order to rank their information content in various 2D scans from the 3D point cloud in order to train a support vector machine (SVM). The data of subjects are collected under two conditions. First when they were covered with a thin blanket and second without any blanket. In order to reduce the dimensionality of the feature space, a T-test approach is employed to determine the most dominant set of features in the frequency domain. The proposed recognition approach based on the frequency domain is also compared with an approach using feature vector defined based on skeleton joints. The comparative studies are performed given various scenarios and by a variety of datasets. Through our study, it is shown that our proposed method offers better performance to that of the joint-based method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Barbara P, Ancoli-Israelb S (2001) Sleep disorders in the elderly. Sleep Med 2:99–114

    Article  Google Scholar 

  • Bixler EO, Vgontzas AN, Have TT, Tyson K, Kales A (1998) Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med 157:144–148

    Article  Google Scholar 

  • Bodyframe Class (2014) Microsoft. https://docs.microsoft.com/en-us/previous-versions/windows/kinect/dn772770%28v%3dieb.10%29. Accessed 2 Dec 2016

  • Boulay B, Brémond F, Thonnat M (2006) Applying 3d human model in a posture recognition system. Pattern Recognit Lett 27:1788–1796

    Article  Google Scholar 

  • Dal Mutto C, Zanuttigh P, Cortelazzo GM (2012) Time-of-flight cameras and Microsoft KinectTM. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  • Falie D, Ichim M, David L (2008) Respiratory motion visualization and the sleep apnea diagnosis with the time of flight (ToF) camera. In: International Conference on Visualization, Imaging and Simulation, WSEAS, pp 179–184

  • Fujiyoshi H, Lipton AJ, Kanade T (2004) Real-time human motion analysis by image skeletonization. IEICE Trans Inf Syst 87:113–120

    Google Scholar 

  • Gordon S, Grimmer K, Patricia T (2007) Understanding sleep quality and waking cervico-thoracic symptoms. Internet J Allied Health Sci Pract 5:1–12

    Google Scholar 

  • Hao T, Xing G, Zhou G (2013) iSleep: unobtrusive sleep quality monitoring using smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. ACM, 2013

  • Karlsen K, Herlofson K, Larsen JP, Tandberg M (1999) Influence of clinical and demographic variables on quality of life in patients with Parkinson’s disease. J Neurol Neurosurg Psychiatry 66:431–435

    Article  Google Scholar 

  • Lachat E, Macher H, Mittet M, Landes T, Grussenmeyer P (2015) First experiences with Kinect v2 sensor for close range 3D modelling. Int Arch Photogramm Remote Sens Spatial Inf Sci 40:93

    Article  Google Scholar 

  • Lee J, Hong M, Ryu S (2015) Sleep monitoring system using kinect sensor. Int J Distrib Sens Netw 11:205

    Google Scholar 

  • Liu JJ, Xu W, Huang M-C, Alshurafa N, Sarrafzadeh M, Raut N, Yadegar B (2013) A dense pressure sensitive bedsheet design for unobtrusive sleep posture monitoring. In: 2013 IEEE international conference on pervasive computing and communications (PerCom). IEEE, pp 207–215

  • Luštrek M, Kaluža B (2009) Fall detection and activity recognition with machine learning. Informatica 33:197–204

    Google Scholar 

  • Manzi A, Cavallo F, Dario PA (2016) Neural network approach to human posture classification and fall detection using RGB-D camera. In: Italian Forum of Ambient Assisted Living

  • Menon A, Kumar M (2013) Influence of body position on severity of obstructive sleep apnea: a systematic review. ISRN Otolaryngol 2013:670381

    Google Scholar 

  • Metsis V, Kosmopoulos D, Athitsos V, Makedon F (2014) Non-invasive analysis of sleep patterns via multimodal sensor input. Pers Ubiquitous Comput 18:19–26

    Article  Google Scholar 

  • Mongkolnam P, Booranrom Y, Watanapa B, Visutarrom T, Chan JH, Nukoolkit C (2017) Smart bedroom for the elderly with gesture and posture analyses using Kinect. Maejo Int J Sci Technol 11:1

    Google Scholar 

  • Oksenberg A, Silverberg DS (1998) The effect of body posture on sleep-related breathing disorders: facts and therapeutic implications. Sleep Med Rev 2:139–162

    Article  Google Scholar 

  • Oksenberg A, Dynia A, Nasser K, Gadoth N (2012) Obstructive sleep apnoea in adults: body postures and weight changes interactions. J Sleep Res 21:402–409

    Article  Google Scholar 

  • Panini L, Cucchiara R (2003) A machine learning approach for human posture detection in domotics applications. In: Proceedings of the 12th international conference on image analysis and processing, 2003. IEEE, pp 103–108

  • Pellegrini S, Iocchi L (2007) Human posture tracking and classification through stereo vision and 3D model matching. EURASIP J Image Video Process 2008:476151

    Google Scholar 

  • Shotton J et al (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56:116–124

    Article  Google Scholar 

  • Sleep Position Gives Personality Clue (2003) BBC. http://news.bbc.co.uk/2/hi/health/3112170.stm. Accessed 20 Sept 2016

  • Sutherland K, Cistulli PA (2015) Recent advances in obstructive sleep apnea pathophysiology and treatment. Sleep Biol Rhythms 13:26–40

    Article  Google Scholar 

  • Theodoridis S, Koutroumbas K (1999) Pattern recognition. Academic Press, New York

    MATH  Google Scholar 

  • Torres C, Hammond SD, Fried JC, Manjunath B (2015) Sleep pose recognition in an ICU using multimodal data and environmental feedback. In: International conference on computer vision systems. Springer, pp 56–66

  • Torres C, Fragoso V, Hammond SD (2016) Eye-CU: sleep pose classification for healthcare using multimodal multiview data. In: 2016 IEEE Winter conference on applications of computer vision (WACV)

  • Wachs J, Goshorn D, Kölsch M (2009) Recognizing human postures and poses in monocular still Images. IPCV 9:665–671

    Google Scholar 

  • Xiao Z, Mengyin F, Yi Y, Ningyi L (2012) 3D human postures recognition using kinect. In: 2012 4th international conference on intelligent human–machine systems and cybernetics (IHMSC). IEEE, pp 344–347

  • Yang C, Mao Y, Cheung G, Stankovic V, Chan K (2014) Graph-based depth video denoising and event detection for sleep monitoring. In: 2014 IEEE 16th international workshop on multimedia signal processing (MMSP). IEEE, pp 1–6

  • Yao K-W, Cheng C (2008) Relationships between personal, depression and social network factors and sleep quality in community-dwelling older adults. J Nurs Res 16:131–139

    Article  Google Scholar 

  • Yoshino A, Nishimura H (2016) Study of posture estimation system using infrared camera. In: International conference on human–computer interaction. Springer, pp 553–558

Download references

Author information

Authors and Affiliations

  1. Networked Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, Canada

    Maryam S. Rasouli D & Shahram Payandeh

Authors
  1. Maryam S. Rasouli D
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahram Payandeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam S. Rasouli D.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rasouli D, M.S., Payandeh, S. A novel depth image analysis for sleep posture estimation. J Ambient Intell Human Comput 10, 1999–2014 (2019). https://doi.org/10.1007/s12652-018-0796-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0796-1

Keywords

Search

Navigation