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IJSTR >> Volume 9 - Issue 8, August 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Restless Leg Syndrome Detection Using Kinect Sensor

[Full Text]

 

AUTHOR(S)

Nada Nabilah Hernawati, Fiky Yosef Suratman, Achmad Rizal

 

KEYWORDS

Polysomnography, restless leg syndrome, Microsoft Kinect Sensor v.2, Sleep Disorder

 

ABSTRACT

Sleep activity is a critical factor in determining the quality of human life. Sleep activity is closely related to sleep quality, which is influenced by several factors, including daily activities, physical conditions, and emotional conditions. The sleep monitoring device that is commonly used is polysomnography. This device is commonly used to monitor sleep by attaching electrodes to the patient's head. This tool's weakness is the feeling of discomfort in the patient, resulting in disruption of sleep monitoring analysis because too many devices are attached to the patient's body. Sleep Disorder is a disorder that makes it difficult for sufferers to regulate their sleep patterns. There are several characteristics for people with sleep disorders: not fresh when waking up, fast drowsiness, difficulty concentrating, fatigue, and memory that continues to decline. In this study, a sleep pattern monitoring system was built using Microsoft Kinect Sensor v.2 for Restless Leg Syndrome (RLS). This device has sensors that can capture every movement of movements produced by the human body. Among the indicators to determine sleep disorders are sleep breathing and sleep posture. The output of this sleep disturbance detection system is a change in the movement of nine joints. The system test has a duration of 120 minutes and changes in subject joint movement per 5 seconds. Sleep disorders are classified into three parts: mild, moderate, and severe, based on the PLMS index. PLMS index values were obtained based on the value of joint movement divided by total sleep time. The system designed has a relative error value of 0.39% in determining the PLMS index.

 

REFERENCES

[1] J. Lee, M. Hong, and S. Ryu, "Sleep Monitoring System Using Kinect Sensor," Int. J. Distrib. Sens. Networks, vol. 2015, pp. 1–9, 2015.
[2] A. Tolaymat and Z. Liu, "Sleep Disorders in Childhood Neurological Diseases," Children, vol. 4, no. 84, pp. 1–11, 2017.
[3] F. Deng et al., "Design and implementation of a noncontact sleep monitoring system using infrared cameras and motion sensor," IEEE Trans. Instrum. Meas., 2018.
[4] B. Jafari and V. Mohsenin, "Polysomnography," in Encyclopedia of Sleep, 2013.
[5] A. A. Khatami, M. A. C. Sebayang, A. Rizal, and D. T. Barus, "Obstructive Sleep Apnea Detection using ECG Signal: A Survey," Technol. Reports Kansai Univ., vol. 62, no. 04, pp. 1267–1274, 2020.
[6] N. Febriana, A. Rizal, and E. Susanto, "Sleep monitoring system based on body posture movement using Microsoft Kinect sensor," in 3rd Biomedical Engineering's Recent Progress in Biomaterials, Drugs Development, and Medical Devices, 2019, vol. 020012, no. April, p. 020012.
[7] A. Roebuck et al., "A review of signals used in sleep analysis," Physiol. Meas., vol. 35, no. 1, pp. R1–R57, Jan. 2014.
[8] Z. Zhang, "Microsoft kinect sensor and its effect," IEEE Multimedia. 2012.
[9] C. L. Comella, "Treatment of Restless Legs Syndrome," Neurotherapeutics. 2014.
[10] L. Klingelhoefer, K. Bhattacharya, and H. Reichmann, "Restless legs syndrome," Clin. Med. J. R. Coll. Physicians London, 2016.
[11] M. M. Ohayon, R. O. Hara, and M. V Vitiello, "Epidemiology of restless legs syndrome : A synthesis of the literature," Sleep Med. Rev., vol. 16, no. 4, pp. 283–295, 2012.
[12] D. B. Guerrero and L. B. Villaluenga, “Microsoft Kinect,” in Universidad Politécnica de Catalunya, 2013, pp. 1–15.
[13] L. Costa, P. Trigueiros, and A. Cunha, "Automatic Meal Intake Monitoring Using Hidden Markov Models," Procedia Comput. Sci., vol. 100, pp. 110–117, 2016.
[14] A. Culebras, "Sleep Disorder and Neurologic Disease," in Sleep disorder and neurologic disease, M. Dekker, Ed. New York: Basel, 2007, pp. 173-203.