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The rapid development of flexible electronic technology has driven innovation in wearable respiratory monitoring devices, but there are still challenges in achieving medical-grade accuracy in quantitative pulmonary function assessment. This study integrates water molecule-responsive flexible sensing technology, wearable devices, and cloud-based intelligent analysis platform to develop the first medical-grade flexible respiratory sensing system (SFMS). By utilizing the synergistic effect of bionic microcavity differential pressure sensing and humidity-sensitive interfaces, combined with a pressure difference-flux dynamic model, the system can simultaneously resolve peak expiratory flow (PEF) and forced vital capacity (FVC), accurately obtaining core pulmonary function indicators such as FEV1/FVC. Clinical validation of 454 cases demonstrates high consistency with gold-standard spirometry (intraclass correlation coefficient [ICC] = 0.93–0.97), with a sensitivity of 89.7% and specificity of 92.3% in differentiating chronic obstructive pulmonary disease (COPD) from asthma. Technologically, this work pioneers a medical-grade flexible sensor for quantitative pulmonary testing, and eliminates dependence on specialized operators through an embedded edge computing architecture that supports real-time cloud data interaction. The system establishes disease-specific profiles through multi-parametric physiological correlation analysis. Practically, its low cost, portability, and user-friendly operation facilitate seamless integration into primary healthcare and home health management, providing technical tools for hierarchical diagnosis and treatment of chronic respiratory diseases. Aligned with WHO's Respiratory Health Action Plan, this innovation enables universal monitoring to advance early screening and long-term disease management. As this innovation possesses significant clinical translation potential, it provides a groundbreaking solution for building a comprehensive prevention and control framework for respiratory diseases.
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Keywords:
- flexible sensing /
- respiratory waves /
- quantitative monitoring /
- lung diseases
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图 1 基于分子间力的柔性呼吸传感器 (I) 传感原理, 水分子在(a)纤维与(b)多孔材料衬底上的吸附与解吸会引起电阻或电容变化[1,3]; (II) 柔性湿度电阻式呼吸传感器潮气敏感介观分子间力, 蚕丝分子结晶抓住石墨烯, 蚕丝分子在潮气作用下, 拉伸、收缩石墨烯导电网络, 引起石墨烯导电网络断开(a)、连接(b)
Figure 1. Flexible humidity sensor based on intermolecular forces: (I) Sensing principle, the adsorption and desorption of water molecules on (a) fibrous and (b) porous substrates can cause changes in resistance or capacitance[1,3]; (II) flexible humidity resistance respiratory sensor moisture sensitive mesoscopic intermolecular force: Silk molecule crystals grasp graphene, and silk molecules stretch and contract the graphene conductive network under the action of moisture, causing the graphene conductive network to disconnect (a) and reconnect (b).
图 2 本研究中开发的整体系统示意图, 基于柔性传感的定量呼吸波系统由3个主要组件组成, 即穿戴式肺功能检测仪、云平台和移动终端
Figure 2. The overall system schematic developed in this study, the quantitative respiratory wave system based on flexible sensing consists of three main components: digital mask, cloud platform, and mobile terminal.
图 3 呼吸传感器在不同环境湿度下的电容-湿度迟滞回线, 从低相对湿度环境(RH = 61.6%)进入高相对湿度环境(RH = 86%, 91%, 97%, 97.5%)时的响应曲线, 并在稳定状态后返回原环境, 可看出吸附曲线与解吸曲线几乎重合, 说明该类传感器对潮气的吸收与解吸完是可逆的, 该性能对呼吸功能的测量一致性尤为重要, 实验系统与步骤见文献[1,2]
Figure 3. The capacitance humidity hysteresis loop of a respiratory sensor under various environmental humidity, the response curve when entering a high relative humidity environment (RH = 86%, 91%, 97%, 97.5%) from a low relative humidity environment (RH = 61.6%), and returning to the original environment after a stable state, it can be seen that the absorption and solution absorption curves almost overlap, this indicates that the absorption and desorption of moisture by this type of sensor are reversible, this performance is particularly important for the measurement consistency of respiratory function. The experimental system and steps can be found in Refs. [1,2].
图 4 (a) 封闭式数字口罩外观与结构; (b) 穿戴式肺功能检测仪将气流的呼吸波定量转化为电信号呼吸波
Figure 4. (a) Appearance and structure of a closed digital hood; (b) the digital mask quantitatively converts the respiratory wave of the airflow into an electrical signal respiratory wave.
图 5 穿戴式肺功能检测仪定量测量原理与模拟呼吸湿度测试及肺功能测试结果 (a) 穿戴式肺功能检测仪用于 PFT 的原理示意图, 此图为传统 PFT 测试流程, 该流程需要专业指导; (b)定量呼吸传感的原理示意图, 以呼气为例, 呼吸湿度进入穿戴式肺功能检测仪, 使内部压力升高. 由于压差作用, 湿度通过空气滤膜排出, 该动态过程遵循达西定律; (c)呼吸波形及相关定量物理参数示意图, ΔC 表示单次呼吸波形的最低点与最高点之差, 基于此关系计算 RH 曲线(插图所示), 并进一步确定呼气过程中相对湿度变化速率的最大值, 即 Max(dη/dt); (d) Max(dη/dt)与最大呼气流量(PEF)的定量关系, 结果表明, Max(dη/dt)与PEF呈线性关系(R2 = 0.9993), 表明呼吸流速可以通过电信号变化率来计算; (e) ΔC与用力肺活量(FVC)的定量关系, 结果表明, 在150—600 L/min的呼气流速范围内, FVC与ΔC成正比, 因此, 基于计算得出的PEF, 可以进一步根据电信号变化来确定呼吸量; (f) ΔC与潮气量(VT)的定量关系[2], 结果表明, 在12—24 L/min的呼气流速范围内, VT与ΔC成正比, 符合人类典型的潮气呼吸流速; (g) 使用穿戴式肺功能检测仪获得的肺功能测试呼吸波形, 测试协议包括受试者首先进行正常潮气呼吸, 然后进行深吸气并随后最大呼气, 将尽可能多的气体排出肺部; (h) 基于与呼吸波形(g)定量关系计算的肺功能测试呼气容积-时间曲线
Figure 5. The principle of quantitative measurement of digital masks and the results of simulated respiratory humidity testing and pulmonary function testing: (a) Schematic diagram of the principle of using masks for PFT, the illustration shows the traditional PFT testing process, which requires professional guidance; (b) schematic diagram of the principle of quantitative respiratory sensing, taking exhalation as an example, breathing humidity into the mask increases the internal pressure, due to the pressure difference, humidity is expelled through the air filter membrane, and this dynamic process follows Darcy's law; (c) schematic diagram of respiratory waveform and related quantitative physical parameters, ΔC represents the difference between the lowest and highest points of a single breath waveform, based on this relationship, calculate the RH curve (as shown in the illustration) and further determine the maximum rate of relative humidity change during exhalation, namely Max (dη/dt); (d) the quantitative relationship between Max (dη/dt) and maximum expiratory flow rate (PEF), the results indicate a linear relationship between Max (dη/dt) and PEF (R2 = 0.9993), suggesting that respiratory flow rate can be calculated through the rate of change of electrical signals; (e) the quantitative relationship between ΔC and forced vital capacity (FVC), the results indicate that FVC is proportional to ΔC within the expiratory flow rate range of 150–600 L/min, therefore, based on the calculated PEF, respiratory volume can be further determined according to changes in electrical signals; (f) the quantitative relationship between ΔC and tidal volume (VT)[2], the results indicate that within the range of 12–24 L/min expiratory flow rate, VT is proportional to ΔC, which is consistent with the typical tidal respiratory flow rate in humans; (g) respiratory waveform obtained from lung function test using a mask. The testing protocol includes the subject first performing normal tidal breathing, followed by deep inhalation and then maximum exhalation to expel as much gas as possible from the lungs; (h) pulmonary function test expiratory volume time curve calculated based on quantitative relationship with respiratory waveform (g).
图 6 柔性传感呼吸功能检测设备(飞星谱设备-本文设计的设备)与市面上的肺功能设备(红象设备)在测量呼吸功能参数FEV1 (a), FVC (b), PEF (c)的比较
Figure 6. Comparison of the measurement of respiratory function parameters FEV1 (a), FVC (b), and PEF (c) between flexible sensing respiratory function testing equipment (Feixing spectrum equipment) and commercially available lung function equipment (Hongxiang equipment).
图 7 柔性传感呼吸功能设备(飞星谱设备-本文设计的设备)4个肺功能参数测量值与PowerCube-Body FEV1 (a), FVC (b), FEV1%pred (c) 与FEV1/FVC (d)测量值比较, 深蓝色点为女性志愿者, 橙色点为男性志愿者
Figure 7. Comparison of four lung function parameter measurements with PowerCube Body FEV1 (a), FVC (b), FEV1% pred (c), and FEV1/FVC (d) measurements using a flexible sensing respiratory function device (Feixing Spectral Device), the dark blue dots represent female volunteers, while the orange dots represent male volunteers.
图 8 柔性传感呼吸功能设备(飞星谱设备-本文设计的设备)与PowerCube-Body 设备对四个肺功能参数FEV1%pred (a) 与FEV1/FVC (b)测量值比较, 深蓝色点为非COPD志愿者, 橙色点为COPD患者
Figure 8. Comparison of FEV1% pred (a) and FEV1/FVC (b) measurements of four lung function parameters between a flexible sensing respiratory function device (Feixing Spectrum device) and a PowerCube Body device. The dark blue dots represent non COPD volunteers, and the orange dots represent COPD patients.
表 1 分子间作用呼吸传感器
Table 1. Molecular interaction respiratory sensors.
类型 原理 核心材料 优点 缺点 柔性湿变电阻式
呼吸传感器
(Flexible
humistor)[40–43]导电材料的电阻随呼吸潮气分压的变化而变化; 第一种材料体系如, 石墨烯/蚕丝复合材料; 这里, 蚕丝材料由潮气引起的循环结构, 引起石墨烯网络断开/重连, 使其电阻随呼吸潮气的分压而变化 蚕丝、碳纳米管、石墨烯的介观杂化材料 在呼吸传感的中高湿度区, 有很高的湿度变化感知性与反应速度; 可水洗、无运动伪影; 原则上, 无限次循环 在进行定量测量过程中, 开放环境对测量结果影响较大 柔性湿变电容式传感
(Capacitive humidity sensor)[44–46]这种传感器与和电介质的相对介电常数在不同潮气下的数值的变化有关 特殊纤维与介质材料 在呼吸传感的中高湿度区, 有很高的湿度变化感知性与反应速度; 可水洗、无运动伪影; 原则上, 无限次循环 在进行定量测量过程中, 开放环境对测量结果影响较大 
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表 2 柔性传感呼吸功能设备(飞星谱设备-本文设计的设备)与红象设备测量肺功能参数对比统计与相关性分析
Table 2. Comparison, statistics and correlation analysis of lung function parameters measured by flexible sensing respiratory function equipment (Feixing spectrum equipment) and red imaging equipment.
误差分析项目 FEV1 FVC PEF 相对误差百分比
的平均值/%8.14 7.12 12.23 测量误差 0.085±0.264 0.085±0.320 0.000±0.691 皮尔森相关系数 0.944 0.935 0.957 ICC3 0.944 0.933 0.956
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表 3 柔性传感呼吸功能设备(飞星谱设备-本文设计的设备)与PowerCube-Body设备测量肺功能参数对比统计与相关性分析
Table 3. Comparison, statistics and correlation analysis of lung function parameters measured by flexible sensing respiratory function equipment (Feixing spectrum equipment) and PowerCube Body equipment.
误差分析项目 FEV1 FVC FEV1/FVC FEV1%pred 相对误差百分比的平均值/% 7.26 8.15 6.37 9.32 测量误差/% 0.000±0.206 0.000±0.315 0.000±5.7 0.000±8.649 皮尔森相关系数 0.969 0.935 0.921 0.944 ICC3 0.968 0.933 0.917 0.942
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表 A1 本文中使用的柔性潮气传感与其他柔性潮气传感的性能对比
Table A1. Performance comparison between the flexible moisture sensor used in this work and other flexible moisture sensors.
性能对比 本文中使用的
柔性潮气传感其他柔性
潮气传感响应时间/s 3.5 11.7 恢复时间/s 4.0 23.9 湿度范围/% 6—97 11—75
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表 C2 常见柔性呼吸传感器
Table C2. Common flexible respiratory sensors.
类型 原理 核心材料 优点 缺点 柔性压
变阻式呼
吸传感[36]柔性压变阻式呼吸传感器基于材料的压阻效应(piezoresistive effect), 即材料在受到外力形变时, 其电阻值发生改变: 1) 传感结构: 通常由柔性基底材料(如PDMS、PET、聚酰亚胺等)和导电敏感材料(如碳基材料、金属纳米线、导电聚合物)复合而成; 2) 呼吸监测机制: 呼吸时胸腔或腹部的周期性起伏导致呼吸传感器发生微形变. 形变改变导电材料的微观结构(如颗粒间距、导电通路), 从而引起电阻变化 石墨烯-PDMS复合薄膜:
材料体系如, 形变传统材料(石墨烯/PDMS复合材料). 这里, 材料变形引起石墨烯微裂纹结构在形变时断开/重连, 电阻显著变化;
液态金属(镓铟合金): 嵌入微流道中, 形变导致导电通路长度变化(可检测0.1%应变); 其优点是结构简单、响应快(<50 ms); 局限性为易受温度漂移影响, 需温度补偿电路高灵敏度和响应速度: 可检测微小压力变化(如呼吸引起的微米级形变);
低功耗: 被动式传感, 无需外部供电(仅需读取电路);
低成本: 材料易得(如石墨烯、碳纳米管), 制备工艺成熟1)长期稳定性问题. 材料疲劳: 反复形变可能导致导电层断裂或基底老化;环境干扰: 温湿度变化可能影响电阻基线.
2) 信号漂移: 长时间使用后需重新校准.
3) 动态范围限制: 对剧烈呼吸或极端形变的检测精度可能下降.
4) 交叉敏感性: 可能受身体其他部位运动(如咳嗽、翻身)干扰.
5) 信号处理复杂度: 需结合滤波算法消除噪声(如基线漂移、运动伪影)柔性形变电容式呼吸传感器[37] 导电材料的电阻随形变(拉伸/压缩)而变化 银纳米线嵌入弹性纤维 可水洗、适合集成到衣物, 开发透气性银纳米线电极, 解决长期穿戴舒适性问题 灵敏度有限, 测试重复性有限, 不利于长期使用 压电呼吸传感器[37] 压电材料(如PVDF, ZnO纳米线) 在应力作用下产生极化电荷
(Q = d·F, d为压电系数)PVDF/ZnO纳米线阵列 自供能、微秒级响应 测试重复性有限, 不利于长期使用 光纤呼吸
传感器[38,39]光纤布拉格光栅(FBG): 呼吸形变改变光栅周期 → 反射波长偏移 FBG光纤或柔性光子晶体 抗电磁干扰、适合高温/腐蚀环境 系统复杂度高, 成本昂贵
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