A Low-Power Wearable Respiratory Sensor for Non-Invasive Stress Monitoring

• URL: http://arxiv.org/abs/2608.05697v1
• Date: Thu, 06 Aug 2026 07:41:34 GMT
• Title: A Low-Power Wearable Respiratory Sensor for Non-Invasive Stress Monitoring
• Abstract summary: We present a compact non-invasive respiratory sensing system based on a force-sensitive resistor embedded in an abdominal belt.<n>We evaluate the complete sensing pipeline across multiple breathing patterns and body positions.<n>Our results show that the proposed platform enables real-time respiratory monitoring across diverse daily-life scenarios.
• Score: 0.5416422382471174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Respiration provides a continuously available window into physiological state and behavior. However, monitoring it outside controlled settings remains challenging because a wearable system must capture small body deformations while remaining comfortable, low power, and robust to changes in posture and motion. We present a compact non-invasive respiratory sensing system based on a force-sensitive resistor (FSR) embedded in an abdominal belt and integrated with a custom Bluetooth Low Energy acquisition board. The system combines a simple piezoresistive readout with a mechanical holder designed to transfer abdominal expansion to the sensor without analog amplification. We evaluate the complete sensing pipeline across multiple breathing patterns and body positions. In stationary settings, the recorded signals exhibit consistent amplitude changes and recurring peak-to-peak timing across breathing maneuvers; under light movement, these variations remain visible despite motion-induced baseline shifts. We further design a five-phase stress-induction protocol and collect respiratory recordings from 12 participants. Using interpretable time-domain features and standard classifiers, we examine whether the acquired signals distinguish relaxation from stress-induction phases. In this preliminary experiment, the best-performing model achieves 88.0% test accuracy, indicating that the extracted respiratory features distinguish stress-induced phases from relaxation phases in this dataset. Overall, our results show that the proposed platform enables real-time respiratory monitoring across diverse daily-life scenarios and captures respiratory changes that distinguish stress-induction from relaxation phases, supporting its potential for affective-computing applications.

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