Commenced in January 2007
Paper Count: 30464
Classification Method for Turnover While Sleeping Using Multi-Point Unconstrained Sensing Devices
Abstract:Elderly population in the world is increasing, and consequently, their nursing burden is also increasing. In such situations, monitoring and evaluating their daily action facilitates efficient nursing care. Especially, we focus on an unconscious activity during sleep, i.e. turnover. Monitoring turnover during sleep is essential to evaluate various conditions related to sleep. Bedsores are considered as one of the monitoring conditions. Changing patient’s posture every two hours is required for caregivers to prevent bedsore. Herein, we attempt to develop an unconstrained nocturnal monitoring system using a sensing device based on piezoelectric ceramics that can detect the vibrations owing to human body movement on the bed. In the proposed method, in order to construct a multi-points sensing, we placed two sensing devices under the right and left legs at the head-side of an ordinary bed. Using this equipment, when a subject lies on the bed, feature is calculated from the output voltages of the sensing devices. In order to evaluate our proposed method, we conducted an experiment with six healthy male subjects. Consequently, the period during which turnover occurs can be correctly classified as the turnover period with 100% accuracy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131956Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 617
 United Nations, Department of Economic and Social Affairs: “World Population Ageing,” (ST/ESA/SER.A/390), p. 2, 2015.
 L. Liu, Y. Peng, M. Liu, and Z. Huang, “Sensor-Based Human Activity Recognition System with a Multilayered Model Using Time Series Shapelets,” Knowledge-Based Systems, Vol. 90, pp. 138–152, 2015.
 J. Stausberg and E. Kiefer, “Classification of pressure ulcers: A systematic literature review,” in Proc. Connect. Health Humans—NI2009, 10th Int. Congr. Nurs. Inf., 28 Jun–1 Jul. 2009, Helsinki, Finland, pp. 511–515.
 C. C. Hsia, K. J. Liou, A. P. W. Aung, V. Foo, W. Huang, and J. Biswas, “Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system,” 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6231–6134,2009.
 R. Yousefi, S. Ostadabbas, M. Faezipour, M. Farshbaf, M. Nourani, L. Tamil, and M. Pompeo, "Bed posture classification for pressure ulcer prevention", 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7175–7178, 2011.
 P. Barsocchi, “Position Recognition to Support Bedsores Prevention,” IEEE Journal of Biomedical and Health Informatics, Vol. 17, No. 1, pp. 53–39, 2013.
 H. J. Lee, S. H. Hwang, S. M. Lee, Y. G. Lim, and K. S. Park, "Estimation of Body Postures on Bed Using Unconstrained ECG Measurements," IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 6, pp. 985–993, 2013.
 S. A. Shah, N. Zhao, A. Ren, Z. Zhang, X. Yang, J. Yang, and W. Zhao, “Posture Recognition to Prevent Bedsores for Multiple Patients Using Leaking Coaxial Cable,” IEEE Access, Vol. 4, pp. 8065–8072, 2016.
 C.-C. Hsia, Y.-W. Hung, Y.-H. Chiu, and C.-H. Kang, “Bayesian classification for bed posture detection based on kurtosis and skewness estimation,” HealthCom 2008—10th International Conference on e-health Networking, Applications and Services, pp. 165–168, 2009.
 Y. Kurihara, T. Kaburagi, and K. Watanabe, “Sensing Method of Patient’s Body Movement Without Attaching Sensors on the Patient’s Body – Evaluation of “Scratching Cheek,” “Turning Over and Scratching back” and “Scratching Shin,” IEEE Sensors Journal, vol. 16, no. 23, DECEMBER 1, 2016.