Contact pressure monitoring device for sleep studies

Pino, Esteban J.; Paz, Astrid Dorner De la; Aqueveque, Pablo; Chávez, Javier A. P.; Moran, Alejandra A.

doi:10.1109/embc.2013.6610461

Cited by 22 publications

(16 citation statements)

References 15 publications

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“…al. [13] implemented a noninvasive sleep monitoring system using a bed pressure sensor array. The system detects changes in the contact pressure between a subject and the bed and is able to automatically select the sensor with the best respiratory signal, determine the respiratory rate, count number of sleep apneas, and count body position changes through the night.…”

Section: Related Workmentioning

confidence: 99%

Determination of Lying Posture through Recognition of Multitier Body Parts

Kim

Kwon

Hyun-Min

et al. 2019

Wireless Communications and Mobile Computing

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In this paper, we propose a lying posture discrimination algorithm to monitor the behavior pattern of a person lying in bed. Using FSR (force sensing resistor) sensors arranged in a grid structure, three major body parts such as head, shoulder, and hips are identified, and six lying positions are determined based on these body parts. Head, shoulder, and hips are relatively high in pressure and are easy to distinguish due to low movement. In addition, we have effectively limited the search space to identify the above body parts based on standard body dimensions. Experimental results show that there is a correlation between pressure distribution and lying posture. The results of this study can be applied to the monitoring of sleep posture and can also be used as an aid to prevent fall accidents and the occurrence of pressure ulcers of elderly patients who are unable to move.

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Section: Related Workmentioning

confidence: 99%

Determination of Lying Posture through Recognition of Multitier Body Parts

Kim

Kwon

Hyun-Min

et al. 2019

Wireless Communications and Mobile Computing

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“…16 A well-designed mattress often possesses the ability to minimize high-pressure points applied onto the body. 17,18 However, if the mattress is not suited for the person, pressure sores may develop at body regions where the pressure is concentrated.…”

Section: Introductionmentioning

confidence: 99%

Effects of Mattress Material on Body Pressure Profiles in Different Sleeping Postures

Low

Chua

Lim

et al. 2017

Journal of Chiropractic Medicine

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Objectives: This study compared the body contact pressure profiles of 2 types of mattresses: latex and polyurethane. Methods: Twenty participants were required to lie down on the different mattresses in 3 different postures for 6 minutes, and their body contact pressure profiles were recorded with a pressure mat sensor. Results: The data indicated that the latex mattress was able to reduce the peak body pressure on the torso and buttocks and achieve a higher proportion of low-pressure regions compared with the polyurethane mattress. Conclusions: Latex mattress reduced peak body pressure and achieved a more even distribution of pressure compared with polyurethane mattress across different sleeping postures. (J Chiropr Med 2017;16:1-9)

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“…The optimal positioning for the desired physiological signal may also influence the sensing location. For instance, sensors may be placed near the expected chest and abdominal regions of the mattress for respiration analysis, or near the heart for cardiac measurement [26,28,30,[68][69][70]. Strategic placement is especially important for single sensors or arrays that cover only a small area.…”

Section: Contributions To Ambient Sensing In Clinical Carementioning

confidence: 99%

“…Physiological signals have been captured by sensors in the environment in a variety of ways, but most ambient sensing modalities fall broadly into five main categories: pressure systems, Doppler systems, audiovisual systems, thermal systems, and textile electrodes. Pressure systems are generally placed where an individual would exert pressure when they lie, sit, or stand, and have included many modalities of sensors including pneumatic sensors [22,23,31,32,63,67], piezoelectric polymers [24,30,33,73,78,79], load cells [38,39,41,42,72], force sensitive resistors (FSR) [16][17][18]21,68,71], hydraulic sensors [24,66,80], capacitive sensors [14,69], indentation measurement potentiometers [65], electret films and foils [27,40], optical sensors [28,81], and strain gauges [82].…”

Section: Sensor Modalitymentioning

confidence: 99%

“…However, posture classification before selection may be more complex than is warranted and errors in classification would impact the breathing fusion. Temporal methods analyze the fitness of the signals themselves by examining time-domain characteristics such as the minimum kurtosis [68], maximum signal amplitude [82] or maximum autocorrelation [116]. Autocorrelation makes use of the rhythmicity of the signal, which can also be inferred from the frequency spectrum.…”

Section: Sensor Selection Combiningmentioning

confidence: 99%

See 1 more Smart Citation

Robust Ambient Multisensor Signal Fusion Towards Clinical Data Analytics

Holtzman¹

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As ambient systems proliferate, there is an increased need for data-level fusion methods that reflect the challenges of unknown environments, non-deterministic signal behaviours, and movement artifact. Cushioning between the sensor and bed occupant decreases signal to noise power and signal availability, while non-linear signals can masquerade as delayed and reversed signals. The main contributions of this thesis are to study how these challenges affect extraction of a breathing signal from bed-based sensors and to propose more robust fusion techniques.New trend analysis methods effectively corrected polarity reversals, increasing the number of good quality signals by 9% and reducing mean respiratory rate error by 24%. To fuse these signals, selection combining, weighted summation, and blind source separation methods were innovated and compared. None performed best all of the time; some were generally good with some weaknesses, while others had specialized strengths. Contextual ensemble fusion selected the best fusion method in degraded conditions in 55% of records, compared to 36% for the top individual fusion method, providing clinical applications with more reliable data. While sleep medicine is an important application, ambient monitoring is also suited to cognitive medicine and palliative care. Developed methods were applied to monitor patients in palliative care, marking the first long-term, continuous monitoring of this population. Breathing patterns observed in the last weeks of life included Cheyne-Stokes respiration and tachypnea, while breathing variability was associated with survival time.ii

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Contact pressure monitoring device for sleep studies

Cited by 22 publications

References 15 publications

Determination of Lying Posture through Recognition of Multitier Body Parts

Determination of Lying Posture through Recognition of Multitier Body Parts

Effects of Mattress Material on Body Pressure Profiles in Different Sleeping Postures

Robust Ambient Multisensor Signal Fusion Towards Clinical Data Analytics

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