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使用惯性测量单元评估呼吸活动:建模与验证。

Assessing Respiratory Activity by Using IMUs: Modeling and Validation.

机构信息

The Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

Scuola di Ingegneria, Università di Pisa, 56126 Pisa, Italy.

出版信息

Sensors (Basel). 2022 Mar 11;22(6):2185. doi: 10.3390/s22062185.

Abstract

This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach.

摘要

本研究旨在探索基于新型惯性测量单元(IMU)的策略,以估计健康成年人在正常呼吸时躺在床上的呼吸参数。在实验过程中,通过 9 个 IMU 网络和 45 个均匀分布的反射标记,同时采集胸壁运动学。对所有惯性运动学进行分析,以确定最小信号集和 IMU,其线性组合最能匹配光体积描记法测量的潮气量。最后通过留一交叉验证方法对得到的模型进行调整和验证,以评估它们在多大程度上可以准确估计与三个躯干腔室相关的一组呼吸参数。所采用的方法允许我们识别出两种不同的模型。第一种,称为模型 1,依赖于位于腹部腔室和下肋缘的三个 IMU 测量的 3D 加速度。第二种,称为模型 2,仅依赖于位于腹部腔室的两个 IMU 测量的加速度的一个分量。这两个模型都可以准确估计呼吸频率(相对误差 <1.5%)。相反,通过模型 2(相对误差 <5%)和模型 1(相对误差 <5%)可以更准确地评估呼吸相持续时间和潮气量。我们进一步讨论了克服局限性和提高所提出方法整体准确性的可能方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eec/8950860/18752011d114/sensors-22-02185-g001.jpg

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