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The respiratory rate is a vital sign indicating breathing illness. It is necessary to analyze the mechanical oscillations of the patient's body arising from chest movements. An inappropriate holder on which the sensor is mounted, or an inappropriate sensor position is some of the external factors which should be minimized during signal registration. This paper considers using a non-invasive device placed under the bed mattress and evaluates the respiratory rate. The aim of the work is the development of an accelerometer sensor holder for this system. The normal and deep breathing signals were analyzed, corresponding to the relaxed state and when taking deep breaths. The evaluation criterion for the holder's model is its influence on the patient's respiratory signal amplitude for each state. As a result, we offer a non-invasive system of respiratory rate detection, including the mechanical component providing the most accurate values of mentioned respiratory rate.
Determination of accelerometer sensor position for respiration rate detection: initial research
(2022)
Continuous monitoring of a patient's vital signs is essential in many chronic illnesses. The respiratory rate (RR) is one of the vital signs indicating breathing diseases. This article proposes the initial investigation for determining the accelerometric sensor position of a non-invasive and unobtrusive respiratory rate monitoring system. This research aims to determine the sensor position in relation to the patient, which can provide the most accurate values of the mentioned physiological parameter. In order to achieve the result, the particular system setup, including a mechanical sensor holder construction was used. The breathing signals from 5 participants were analyzed corresponding to the relaxed state. The main criterion for selecting a suitable sensor position was each patient's average acceleration amplitude excursion, which corresponds to the respiratory signal. As a result, we provided one more defined important parameter for the considered system, which was not determined before.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.