Seismo-Mat: mattress-embedded inertial sensors for unobtrusive monitoring of heart rate, respiratory rate, and bed occupancy during sleep.

OBJECTIVE: Continuous monitoring of heart rate (HR) and respiratory rate (RR) during sleep may support longitudinal assessment and the identification of clinically relevant trends, while current gold-standard techniques such as polysomnography are intrusive and unsuitable for prolonged home use. This study aims to develop and assess the feasibility of an unobtrusive system for overnight monitoring of HR, RR, and preliminary bed occupancy detection in real-world settings. METHODS: A novel nearable system is presented, based on multiple mattress-embedded inertial sensors capturing the mechanical vibrations and low-frequency movements associated with cardiac and breathing activity. A dedicated signal processing pipeline detects periods of stillness, selects the most informative sensor and axis, and estimates HR and RR, while bed occupancy is identified using machine learning techniques. RESULTS: The system was assessed through controlled laboratory experiments and overnight home recordings against a reference device. Across different body postures, mean absolute errors were below 5 beats per minute for HR and below 2 breaths per minute for RR. In the home recordings, 96% of the analysis windows fell within 5 beats per minute for HR using the gyroscope, against 83% using the accelerometer. CONCLUSION: The results demonstrate the feasibility of multi-parameter sleep monitoring using mattress embedded inertial sensors in both the laboratory setting and unsupervised home conditions. SIGNIFICANCE: The proposed system estimates HR, RR, and bed occupancy simultaneously from mattress-embedded sensors and offers new insights into the role of inertial measurement units in HR and RR monitoring during sleep.