Respiratory Inductance Plethysmography (RIP) is commonly used to detect the respiratory motions of chest and abdomen during sleep. However, this method is not suitable for daily use due to its strong constraints. Many unconstrained methods have been developed but they have a common problem in distinguishing the chest and abdominal motions, which are required for understanding the type of sleep apnea. In this study, we proposed an unconstrained method to distinguish chest and abdominal motions by measuring the pressure fluctuations from the chest and abdomen acting on a mattress. We then investigated the feasibility of detecting respiratory motions by using a flexible tactile sensor array, which was laid on a bed like a bed sheet. Ten healthy subjects (five males and five females) were tested and the results were compared with those measured with RIP band sensors. It was confirmed that the sensor array could measure the motions of the chest and abdomen and obtain the respiratory rate at a high precision. However, the phase of the pressure fluctuations measured with the sensor array might disagree with the outputs of the RIP band sensors, depending on the gender and lying position of the subject. The reason was discussed with a dynamic model, which showed that the disagreement might have been related to the coupling of the chest and abdomen. In addition, the influence of the stiffness of the mattress on the pressure fluctuations was investigated. The results showed that mattress stiffness had little effect on the measurement.

Sleep monitoring systems that can be used in daily life for the assessment of personal health and early detection of diseases are needed. To this end, we are developing a system for unconstrained measurement of the lying posture, respiration and heartbeat of a person on a soft rubber-based tactile sensor sheet. The respiration and heartbeat signals can be detected from only particular locations on the tactile sensor, and the locations depend on the lying location and posture of the measured person. In this paper, we describe how to determine the measurement locations on the sensor. We also report a realtime program that detects the respiration rate and the heart rate by using this method.

Measurement of the sleeping state is useful for monitoring the health of a person being nursed. The sleeping state can be estimated from biological information such as respiration rate, heart rate, body motion, and lying posture. A heart rate measurement method that considers the harmonics of a respiratory signal is described herein. The harmonics of respiratory signals for heart rate measurement has not been considered hitherto. An unconstrained method is proposed for measuring respiration, heart rate, and lying posture using a Smart Rubber sensor, which is a rubber-based flexible planar tactile sensor developed for this study. Respiration and heart rates are measured by applying frequency analysis to time-series data of body pressure. The harmonics of a respiratory signal serves as noise in heart rate measurement. Therefore, the heart rate measurement is improved by eliminating the effects of harmonics. The average frequency error of the heart rate measurement by our proposed method is 0.144 Hz. Experimental results show that our proposed method enhances the precision of heart rate measurement. Hence, this method enables the accurate measurement of the sleeping state.