arXiv:2608.05697q-bio.QMcs.LG2026-08

用腹部传感器实时捕捉呼吸变化,区分压力与放松状态。

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

论文配图:A Low-Power Wearable Respiratory Sensor for Non-Invasive Stress Monitoring
图 1 · 摘自论文原文
  • 采用柔性电阻传感器嵌入腰带,无放大直接感知腹式呼吸
  • 在静止和轻度运动下均能稳定识别呼吸波形,准确率88.0%
  • 适合日常场景下的情绪计算应用,无需侵入性设备

呼吸为生理状态与行为提供了持续可测的窗口。然而,在非受控环境中监测呼吸仍具挑战,因穿戴系统需在保持舒适、低功耗的同时,精准捕捉微小身体形变,并抵抗体位与动作干扰。本文提出一种基于力敏电阻(FSR)的紧凑型非侵入式呼吸传感系统,集成于腹部束带,并搭配定制蓝牙低功耗采集板。系统通过机械结构将腹部膨胀直接传递至传感器,实现无模拟放大的一阶压阻读出。我们在多种呼吸模式与体位下评估完整传感链路:静态环境下信号幅度与峰峰值时间保持一致;轻度运动时虽存在基线漂移,但周期性变化仍清晰可见。进一步设计五阶段压力诱导实验,对12名参与者进行呼吸数据采集。结合可解释的时间域特征与标准分类器,分析信号能否区分放松与压力阶段。初步实验中,最佳模型测试准确率达88.0%,表明所提取呼吸特征可有效区分压力与放松状态。总体结果表明,该平台支持多样化日常场景下的实时呼吸监测,且能捕捉可区分情绪状态的呼吸变化,具备情感计算应用潜力。

原文摘要 · Abstract (English)

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.

可穿戴呼吸监测情绪计算低功耗

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