发表机构
Sharif University of Technology（谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了一种基于FSR的低功耗可穿戴呼吸传感系统，通过实验验证其可区分压力与放松阶段，测试准确率达88.0%，支持情感计算应用。
AI 中文摘要
呼吸为生理状态与行为提供了持续可获取的观测窗口，但在受控环境外对其进行监测仍具挑战性，因为可穿戴系统需捕捉微小的身体形变，同时保持舒适、低功耗，且对姿势和运动变化具有鲁棒性。我们提出一种紧凑的非侵入式呼吸传感系统，该系统基于嵌入腹带的力敏电阻（FSR），并集成定制的蓝牙低功耗（Bluetooth Low Energy）采集板。该系统将简单的压阻读出与机械支架相结合，该支架设计用于将腹部扩张传递至传感器，无需模拟放大。我们在多种呼吸模式和身体姿势下评估完整的传感流程：在静止环境中，所记录信号在呼吸动作中呈现出一致的幅度变化和重复的峰峰值时序；在轻度运动下，尽管存在运动诱发的基线偏移，这些变化仍清晰可见。我们进一步设计了五阶段压力诱导方案，并从12名参与者处采集呼吸记录，使用可解释的时域特征和标准分类器，检验所采集信号是否能区分放松阶段与压力诱导阶段。在该初步实验中，表现最佳的模型达到88.0%的测试准确率，表明在该数据集中，提取的呼吸特征可区分压力诱导阶段与放松阶段。总体而言，我们的结果显示，所提出的平台可在多样化的日常生活场景中实现实时呼吸监测，并捕捉到可区分压力诱导与放松阶段的呼吸变化，为情感计算应用提供支持。
英文摘要
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.
CommentsCode available at this URL: https://github.com/mohamad-hoseini/FSResp
