Laufer Bernhard, Abdulbaki Alshirbaji Tamer, Docherty Paul David, Jalal Nour Aldeen, Krueger-Ziolek Sabine, Moeller Knut
Institute of Technical Medicine (ITeM), Furtwangen University, 78056 Villingen-Schwenningen, Germany.
Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany.
Sensors (Basel). 2025 Apr 10;25(8):2401. doi: 10.3390/s25082401.
The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology through new, improved sensor systems and the use of artificial intelligence have given this field of research a new dynamic in recent years and opened up new possibilities. Based on the measurement from a motion capture system, the respiration-induced surface motions of 16 test subjects were examined, and specific motion parameters were calculated. Subsequently, linear regression and a tailored convolutional neural network (CNN) were used to determine tidal volumes from an optimal set of motion parameters. The results showed that the linear regression approach, after individual calibration, could be used in clinical applications for 13/16 subjects (mean absolute error < 150 mL), while the CNN approach achieved this accuracy in 5/16 subjects. Here, the individual subject-specific calibration provides significant advantages for the linear regression approach compared to the CNN, which does not require calibration. A larger dataset may allow for greater confidence in the outcomes of the CNN approach. A CNN model trained on a larger dataset would improve performance and may enable clinical use. However, the database of 16 subjects only allows for low-risk use in home care or sports. The CNN approach can currently be used to monitor respiration in home care or competitive sports, while it has the potential to be used in clinical applications if based on a larger dataset that could be gradually built up. Thus, a CNN could provide tidal volumes, the missing parameter in vital signs monitoring, without calibration.
几十年来，通过上半身呼吸引起的表面运动来测量潮气量一直是医学诊断的一个目标，但尚未取得真正的突破。近年来，通过新型、改进的传感器系统以及人工智能的应用来改进测量技术，为该研究领域注入了新的活力，并开辟了新的可能性。基于运动捕捉系统的测量，对16名测试对象的呼吸引起的表面运动进行了检查，并计算了特定的运动参数。随后，使用线性回归和定制的卷积神经网络（CNN）从一组最佳运动参数中确定潮气量。结果表明，线性回归方法在个体校准后，可用于13/16名受试者的临床应用（平均绝对误差<150 mL），而CNN方法在5/16名受试者中达到了这一精度。在此，与不需要校准的CNN相比，个体受试者特异性校准为线性回归方法提供了显著优势。更大的数据集可能会让人对CNN方法的结果更有信心。在更大数据集上训练的CNN模型将提高性能，并可能实现临床应用。然而，16名受试者的数据库仅允许在家庭护理或运动中进行低风险使用。目前CNN方法可用于家庭护理或竞技体育中的呼吸监测，而如果基于可逐步建立的更大数据集，它有潜力用于临床应用。因此，CNN可以在无需校准的情况下提供潮气量，这是生命体征监测中缺失的参数。
