This work, in a pioneering approach, attempts to build a biometric system
that works purely based on the fluid mechanics governing exhaled breath. We
test the hypothesis that the structure of turbulence in exhaled human breath
can be exploited to build biometric algorithms. This work relies on the idea
that the extrathoracic airway is unique for every individual, making the
exhaled breath a biomarker. Methods including classical multi-dimensional
hypothesis testing approach and machine learning models are employed in
building user authentication algorithms, namely user confirmation and user
identification. A user confirmation algorithm tries to verify whether a user is
the person they claim to be. A user identification algorithm tries to identify
a user's identity with no prior information available. A dataset of exhaled
breath time series samples from 94 human subjects was used to evaluate the
performance of these algorithms. The user confirmation algorithms performed
exceedingly well for the given dataset with over $97\%$ true confirmation rate.
The machine learning based algorithm achieved a good true confirmation rate,
reiterating our understanding of why machine learning based algorithms
typically outperform classical hypothesis test based algorithms. The user
identification algorithm performs reasonably well with the provided dataset
with over $50\%$ of the users identified as being within two possible suspects.
We show surprisingly unique turbulent signatures in the exhaled breath that
have not been discovered before. In addition to discussions on a novel
biometric system, we make arguments to utilise this idea as a tool to gain
insights into the morphometric variation of extrathoracic airway across
individuals. Such tools are expected to have future potential in the area of
personalised medicines.

该研究通过建立一个基于呼气空气流体力学的生物特征识别系统来验证关于呼气气流结构可以用于建立生物特征算法的假设，并应用多维假设检验和机器学习模型的方法来构建用户验证和用户识别算法，并对 94 名人体样本的呼气时间序列样本数据集进行实验和评估，结果表明该算法具有较高的真实确认率，并揭示了未曾发现的独特呼气气流湍流特征，此外还探讨了该生物特征系统作为一种获得个体之间胸外空气道形态变异见解的工具的潜在用途。

基于人体呼气物理的用户认证系统

User authentication system based on human exhaled breath physics

According to the World Health Organization, the SARS-CoV-2 virus generated a
global emergency between 2020 and 2023 resulting in about 7 million deaths out
of more than 750 million individuals diagnosed with COVID-19. During these
years, polymerase-chain-reaction and antigen testing played a prominent role in
disease control. In this study, we propose a fast and non-invasive detection
system exploiting a proprietary mass spectrometer to measure ions in exhaled
breath. We demonstrated that infected individuals, even if asymptomatic,
exhibit characteristics in the air expelled from the lungs that can be detected
by a nanotech-based technology and then recognized by soft-computing
algorithms. A clinical trial was ran on about 300 patients: the mass spectra in
the 10-351 mass-to-charge range were measured, suitably pre-processed, and
analyzed by different classification models; eventually, the system shown an
accuracy of 95% and a recall of 94% in identifying cases of COVID-19. With
performances comparable to traditional methodologies, the proposed system could
play a significant role in both routine examination for common diseases and
emergency response for new epidemics.

本文提出了一种快速、非侵入性的检测系统，利用专有的质谱仪测量呼出气体中的离子，该系统可以检测到感染 COVID-19 的人呼出的空气中的特征，经过临床试验表明，该系统能以 95% 的准确度和 94% 的召回率识别新冠病例。