Flexible Electrodes-Based Smart Mattress for Monitoring Physiological Signals of Heart and Autonomic Nerves in a Non-Contact Way

Peng, Sijia; Xu, Ke; Bao, Shenjie; Yuan, Yafei; Dai, Chenyun; Chen, Wei

doi:10.1109/jsen.2020.3012697

Cited by 17 publications

(2 citation statements)

References 38 publications

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“…The setup can notice caregivers to chance the position of patient to prevent bedsores. Since rigid PCB circuitry in wearables are not comfortable, Peng et al [22] has built electrodes in a band with flexible silver fiber conductive embedded in patient mattress. Along these lines, a PCB circuit in a patch embedded with pressure, humidity, temperature sensors, and Bluetooth Low Energy to communicate with a single base station was made by Sen at al.…”

Section: Related Workmentioning

confidence: 99%

Designing a Smart Pillow for Sleep Quality Remote Monitoring

Oliveira

Pereira

Carvalho

et al. 2022

Preprint

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This paper proposes a low cost IoT solution to detect head movements and positions of a patient by means of Force Sensing Resistors positioned on a pillow and connected to a micro-controller collecting patient data anytime, when sleeping, sending it to the cloud and making it available to healthcare professionals. The impact of this work is focused on monitoring sleep quality, using low-cost and easy to use pillows in an ambulatory scenario, without the need of expensive and dedicated sleeping rooms for sleep monitoring, which most of the times affect patient sleep and degrades the quality of the measurement.In this case it is possible to monitor the patient’s behavior throughout the entire sleep, important for detecting factors causing minor head and neck injuries and even checking for events of long pauses in respiratory rate.

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Section: Related Workmentioning

confidence: 99%

Designing a Smart Pillow for Sleep Quality Remote Monitoring

Oliveira

Pereira

Carvalho

et al. 2022

Preprint

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show abstract

“…For example, it works without the need of skin preparation and the risk of skin irritation [3], and can be used repeatedly [4]. Therefore, CC electrode has been embedded into diverse objects including toilet seats [5], bathtubs [6], driver's seats [7], chairs [2], bedsheets, and mattresses [8]. It has found a wide range of applications in many scenarios, such as sleep [9], car driving [10] and exercise monitoring [11], as well as during office work and other daily life [12].…”

mentioning

confidence: 99%

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks

Peng

Cui

et al. 2022

BioMed Eng OnLine

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Background Capacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition. Methods Two sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced. Results In the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment. Conclusions The results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

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A Two-Stream Model Combining ResNet and Bi-LSTM Networks for Non-contact Dynamic Electrocardiogram Signal Quality Assessment

Zhu

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Flexible Electrodes-Based Smart Mattress for Monitoring Physiological Signals of Heart and Autonomic Nerves in a Non-Contact Way

Cited by 17 publications

References 38 publications

Designing a Smart Pillow for Sleep Quality Remote Monitoring

Designing a Smart Pillow for Sleep Quality Remote Monitoring

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks

A Two-Stream Model Combining ResNet and Bi-LSTM Networks for Non-contact Dynamic Electrocardiogram Signal Quality Assessment

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