A Scalable and Domain Adaptive Respiratory Symptoms Detection Framework using Earables
A Scalable and Domain Adaptive Respiratory Symptoms Detection Framework using Earables
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DOI：
10.1109/bigdata52589.2021.9671796
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Nhan Nguyen;Avijoy Chakma;Nirmalya Roy
中科院分区：
文献类型：
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作者：
Nhan Nguyen;Avijoy Chakma;Nirmalya Roy
新冠肺炎疫情在全球范围内对人类健康带来了毁灭性的影响，人们仍将口罩作为遏制新冠肺炎传播的预防措施。咳嗽是新冠肺炎的主要传播媒介之一，及早发现咳嗽在阻止S传播危及生命的病毒方面发挥了重要作用。在文献中已经提出了许多方法来开发检测咳嗽和其他呼吸道症状的系统，但可穿戴设备在检测呼吸道症状方面还没有得到很好的研究和调查。在这项工作中，我们提出了一个声学研究原型(可穿戴设备)-eSense，它将声学和IMU传感器嵌入到用户方便的耳塞中，以解决以下问题：(I)可穿戴设备在检测呼吸道症状方面的可行性，以及(Ii)在存在未见数据样本的情况下训练的机器学习模型的可扩展性。我们在eSense收集的数据样本上进行了浅层学习模型和深度学习模型的实验。我们观察到，深度学习模型的性能优于浅层学习模型，达到了97%的准确率。此外，我们研究了深度学习模型在不可见数据集上的可扩展性，并注意到当在特定数据集上训练和在不可见数据集上测试时，深度学习模型的性能恶化。为了缓解这些挑战，我们假设了一种对抗性领域适应技术，该技术有助于大幅提高我们的呼吸道症状检测框架的性能。
The COVID-19 pandemic has brought a devastating impact on human health across the globe, and people are still observing face-masking as a preventive measure to contain the spread of COVID-19. Coughing is one of the major transmission mediums of COVID-19, and early cough detection could play a significant r ole i n p reventing t he s pread o f t his life-threatening virus. Many approaches have been proposed for developing systems to detect coughing and other respiratory symptoms in literature, but earable devices are not well-studied and investigated for respiratory symptom detection. In this work, we posited an acoustic research prototype (earable device) - eSense that has acoustic and IMU sensors embedded into user-convenient earbuds to address the following issues: (i) feasibility of the earables in detecting respiratory symptoms, and (ii) scalability of trained machine learning models in the presence of unseen data samples. We performed experimentation with both shallow and deep learning models on the eSense collected data samples. We observed that the deep learning model outperforms the shallow learning models achieving 97% accuracy. Furthermore, we investigated the scalability of the deep learning model on unseen datasets and noticed that the performance of the deep learning model deteriorates when trained on a particular dataset and tested on an unseen dataset. To mitigate such challenges, we postulated an adversarial domain adaptation technique that helps improve the performance of our respiratory symptoms detection framework by a substantial margin.