Towards an Intelligent Bed Sensor: Non-intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques

Malakuti, Kaveh; Albu, Alexandra Branzan

doi:10.1109/icpr.2010.974

Cited by 16 publications

(11 citation statements)

References 5 publications

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“…With the recent developments, wireless sensing has opened the doors for sleep monitoring systems leveraging various sensors, such as audio, image, force and temperature [8][9][10][11][12][13][14][15][16][17][18]. Most of these wireless systems are contact-oriented require wearable sensors worn by the patient to acquire adequate precision levels in a clinical setting.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Diagnosis of the Hypopnea syndrome in the early stage

Yang

Fan

Ren

et al. 2019

Neural Comput & Applic

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Hypopnea syndrome is a chronic respiratory disease that is characterized by repetitive episodes of breathing disruptions during sleep. Hypopnea syndrome is a systemic disease that manifests respiratory problems, however, more than 80% of Hypopnea syndrome patients remain undiagnosed due to complicated polysomnography. Objective assessment of breathing patterns of an individual can provide useful insight into the respiratory function unearthing severity of Hypopnea syndrome. This paper explores a novel approach to detect incognito Hypopnea syndrome as well as provide a contactless alternative to traditional medical tests. The proposed method is based on S-Band sensing, peak detection algorithm and sine function fitting for the observation of breathing patterns and characterization of normal or disruptive breathing patterns for Hypopnea syndrome detection. The proposed system observes the human subject and changes in the channel frequency response caused by Hypopnea syndrome utilizing a wireless link between two monopole antennas, placed 3m apart. Accuracy of the proposed method is established through comparison with commercial respiratory sensor. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate Hypopnea syndrome monitoring in a patient friendly and flexible environment.

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Section: Introductionmentioning

confidence: 99%

“…Force sensors deployed under the mattress top have also been used to detect the heart rate, sleep pattern, snoring, or respiration rate [11][12][13][14][15]. Martinez et al have investigated a wireless sensor system to detect respiratory rate using received signal strength indicator [16].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosis of the Hypopnea syndrome in the early stage

Yang

Fan

Ren

et al. 2019

Neural Comput & Applic

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“…In addition, there is a non-contact method which uses a microphone and an infrared sensor to monitor the sleep status. Moreover, some studies utilize motion sensors (accelerometer, piezoelectric sensor) inside the pillow (Harada et al, 2000) or bed (Malakuti et al, 2010;Hoque et al, 2010) to monitor the sleep movement and sleep positions.…”

Section: Sleep Cyclementioning

confidence: 99%

Breath and Position Monitoring During Sleeping With a Depth Camera

Yu¹,

Wu²,

Liou³

et al. 2012

Proceedings of the International Conference on Health Informatics

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Sleep monitoring is increasingly seen as a common and important issue. In this paper, a depth analysis technique was developed to monitor user's sleep conditions without any physical contact. In this research, a cross-section method was proposed to detect user's head and torso from the depth images. Then, the system can monitor user's breathing rate, sleep position, and sleep cycle. In order to evaluate the measurement accuracy of this system, two experiments were conducted. In the first experiment, eight participants with various body shapes were asked to join the experiment. They were asked to change the sleep positions (supine and side-lying) every fifteen breathing cycles in two circumstances (sleep with and without a thin quilt) on the bed. The experimental results showed that the system is promising to detect the head and torso with various sleeping postures. In the second experiment, a realistic overnight sleep monitoring experiment was conducted. The experimental results demonstrated that this system is promising to monitor the sleep conditions in realistic sleep conditions. To conclude, this study is important for providing a non-contact technology to detect multiple sleep conditions and assist users in better understanding of their sleep quality.

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“…The easiest way to monitor people is to use vision cameras [8]. It can be used for fall detection [9,10], sleep monitoring [11,12], breathing detection [13,14], depression detection [15,16], measuring vital signs [17,18], and posture detection [19,20]. However, they can have blind spots because of the camera’s position.…”

Section: Introductionmentioning

confidence: 99%

Patient Posture Monitoring System Based on Flexible Sensors

Cha

Nam

Kim

2017

Sensors

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Monitoring patients using vision cameras can cause privacy intrusion problems. In this paper, we propose a patient position monitoring system based on a patient cloth with unobtrusive sensors. We use flexible sensors based on polyvinylidene fluoride, which is a flexible piezoelectric material. The flexible sensors are inserted into parts close to the knee and hip of the loose patient cloth. We measure electrical signals from the sensors caused by the piezoelectric effect when the knee and hip in the cloth are bent. The measured sensor outputs are transferred to a computer via Bluetooth. We use a custom-made program to detect the position of the patient through a rule-based algorithm and the sensor outputs. The detectable postures are based on six human motions in and around a bed. The proposed system can detect the patient positions with a success rate over 88 percent for three patients.

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Towards an Intelligent Bed Sensor: Non-intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques

Cited by 16 publications

References 5 publications

RETRACTED ARTICLE: Diagnosis of the Hypopnea syndrome in the early stage

RETRACTED ARTICLE: Diagnosis of the Hypopnea syndrome in the early stage

Breath and Position Monitoring During Sleeping With a Depth Camera

Patient Posture Monitoring System Based on Flexible Sensors

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