Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones

Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones
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通过商品智能手机上的声学传感和多任务学习进行临床外肺部疾病评估

DOI:
10.1145/3560905.3568437
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发表时间:
2022
期刊:
Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
影响因子:
--
通讯作者:
Gao, Wei
Gao, Wei
中科院分区:
--
文献类型:
--
作者:
Yin, Xiangyu;Huang, Kai;Forno, Erick;Chen, Wei;Huang, Heng;Gao, Wei

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相似文献

诸如哮喘和慢性阻塞性肺疾病(COPD)的肺部疾病构成了重大的公共卫生挑战。包括气道阻塞和炎症的疾病症状通常导致气道机械特性的变化,例如气道的口径和阻抗。为了测量用于疾病评估和诊断目的的这种气道特性,肺功能测试(PFT)已被广泛采用。然而,大多数现有的PFT系统需要昂贵且笨重的硬件,这些硬件不可能在诊所外使用。为了允许门诊连续肺部疾病评估,在本文中,我们提出了AWARE,这是一种新的传感和AI系统,可以使用商品智能手机支持准确可靠的PFT。AWARE使用智能手机传输声学信号,并基于对从智能手机麦克风捕获的反射声波的分析来重建人体气道的轮廓。然后通过多任务学习模型评估受试者的肺部状况,该多任务学习模型将气道测量结果和受试者的肺功能记录两者整合为基础事实。对75名人类受试者的评估表明,AWARE有能力在区分具有健康肺功能和哮喘症状的人方面达到80%的准确率。
Pulmonary diseases, such as asthma and Chronic Obstructive Pulmonary Disease (COPD), constitute a major public health challenge. The disease symptoms, including airway obstruction and inflammation, usually result in changes in airway mechanical properties, such as the caliber and impedance of the airway. To measure such airway properties for disease evaluation and diagnosis purposes, pulmonary function tests (PFT) has been widely adopted. However, most existing PFT systems require expensive and cumbersome hardware that are impossible to be used out of clinic. To allow out-clinic continuous pulmonary disease evaluation, in this paper we present AWARE, a new sensing and AI system that supports accurate and reliable PFT using commodity smartphones. AWARE uses a smartphone to transmit acoustic signals and reconstructs the profile of human airway based on the analysis of reflected acoustic waves captured from the smartphone's microphone. The subject's pulmonary condition is then evaluated by a multi-task learning model that integrates both the airway measurements and the subject's lung function records as the ground truth. Evaluations on 75 human subjects demonstrate that AWARE has the capability to achieve 80% accuracy on distinguishing between humans with healthy pulmonary function and with asthma symptoms.
DOI: 10.1088/0143-0815/12/2/002
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