Sleep Posture Recognition for Bedridden Patient

  • Nitikorn Srisrisawang
  • Lalita NarupiyakulEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 513)


One type of patients that needs to live on the bed for a certain time or worst, for the rest of their life is called bedridden. This type of patients need special attention from caretaker to regularly change the posture of the patient in order to prevent symptom named bed sore or pressure sore which will happen when the weight of the patient is applied to some points of the body too long which leads to injury to that certain points of the body. This research will carried out to design a system to relieve the work for the caretaker of a bedridden patient. This system consists of three parts; Sleep data collection where computer that connected to Kinect will continuously monitor the patient and send the data to the next part, Sleep posture analysis which will determine the postures of the patient from the input data, and Sleep notification part which will notify user with the current state of the patient. There are 3 machine learning algorithms that were chosen to compare their performance; Decision Tree (DT), Neural Network (NN), and Support Vector Machine (SVM). In the case of using the data from the same subjects as in the training set, DT shows lower accuracy at 93.33% than NN and SVM which achieve 100%. Similarly, in the case of using dataset that is not in the training set, DT still performs at 90% while both NN and SVM achieve 100%, the data are tested from both the subjects within the training set and new subjects but without any error exclusion which illustrates that NN which achieves 63.33% accuracy is more prone to the data with error than SVM which is 57.78%. Hence, NN is implemented with the system.


Kinect Bed sore Bedridden Sleep posture Recognition 



This research is financially supported by Crown Property Bureau Funding, Thailand.


  1. 1.
    Bains P, Minhas AS (2011) Profile of Home-based Caregivers of Bedridden Patients in North India. Indian J Community Med 36:114–119. Scholar
  2. 2.
    Khoury RM, Camacho-Lobato L, Katz PO et al (1999) Influence of spontaneous sleep positions on nighttime recumbent reflux in patients with gastroesophageal reflux disease. Am J Gastroenterol 94:2069–2073. Scholar
  3. 3.
    Beattie ZT, Hagen CC, Hayes TL (2011) Classification of lying position using load cells under the bed. In: 2011 annual international conference on IEEE engineering in medicine and biology society IEEE, pp 474–477Google Scholar
  4. 4.
    Yousefi R, Ostadabbas S, Faezipour M et al (2011) Bed posture classification for pressure ulcer prevention. In: annual international conference on IEEE engineering in medicine and biology society IEEE, pp 7175–7178Google Scholar
  5. 5.
    Grimm T, Martinez M, Benz A, Stiefelhagen R (2016) Sleep position classification from a depth camera using Bed Aligned Maps. In: 2016 23rd international conference on pattern recognition IEEE, pp 319–324Google Scholar
  6. 6.
    Huang W, Wai AAP, Foo SF et al (2010) Multimodal sleeping posture classification. 2010 20th International conference on pattern recognition 4336–4339.
  7. 7.
    Torres C, Hammond SD, Fried JC, Manjunath BS (2015) Sleep pose recognition in an icu using multimodal data and environmental feedback. Springer, Cham, pp 56–66Google Scholar
  8. 8.
    Team A (2016) AzureML: Anatomy of a machine learning service. In: Dorard L, Reid MD, Martin FJ (eds) Proc. 2nd international conference on prediction APIs Apps, PMLR, Sydney, Australia, pp 1–13Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Integrative Computational Bioscience CenterMahidol UniversitySalayaThailand
  2. 2.Department of Computer Engineering, Faculty of EngineeringMahidol UniversitySalayaThailand

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