# A Testbed for the Development and Validation of Contactless Vital Signs Monitoring Systems

## TL;DR

This paper introduces a programmable testbed for generating realistic heart and respiratory rate signals to improve contactless vital signs monitoring systems.

## Contribution

A single-motor-based testbed that generates realistic cardiorespiratory signals across a wide physiological range.

## Key findings

- The synthetic signals correlate strongly (0.85) with data from 75 human subjects.
- The system supports the development of robust algorithms for extreme physiological ranges.
- It reduces the need for extensive real-world data collection for system validation.

## Abstract

Contactless monitoring of vital signs such as heart rate (HR) and respiratory rate (RR) has gained significant attention, with vibration-based sensors like geophones showing promise for accurate, non-invasive monitoring. However, most existing systems are developed with healthy subjects and may not generalize well to extreme physiological ranges, such as those observed in infants or patients with arrhythmia. Moreover, the underlying mechanisms of cardiorespiratory vibration dynamics remain insufficiently understood, limiting clinical adoption of these systems. To address these challenges, we present a programmable cardiorespiratory testbed capable of generating realistic HR and RR signals across a wide range (HR: 40–240 bpm, RR: 8–40 bpm). Our system uses a voice coil motor that acts as the vibration source, driven by a Raspberry Pi-based control circuit. Unlike similar systems that use separate modules for heart and lung signals, our setup generates both signals using a single motor. The synthetic signals exhibit a strong correlation of 0.85 compared with data from 75 human subjects. We use this system to design signal processing-based algorithms for vital signs monitoring and demonstrate their robustness for extreme physiological ranges. The proposed system enhances the understanding of cardiorespiratory vibration dynamics while significantly reducing the time and effort required to collect real-world data.