Unobtrusive Air Leakage Estimation for Earables with In-ear Microphones

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3631405
B. U. Demirel, Ting Dang, Khaldoon Al-Naimi, F. Kawsar, A. Montanari
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Abstract

Earables (in-ear wearables) are gaining increasing attention for sensing applications and healthcare research thanks to their ergonomy and non-invasive nature. However, air leakages between the device and the user's ear, resulting from daily activities or wearing variabilities, can decrease the performance of applications, interfere with calibrations, and reduce the robustness of the overall system. Existing literature lacks established methods for estimating the degree of air leaks (i.e., seal integrity) to provide information for the earable applications. In this work, we proposed a novel unobtrusive method for estimating the air leakage level of earbuds based on an in-ear microphone. The proposed method aims to estimate the magnitude of distortions, reflections, and external noise in the ear canal while excluding the speaker output by learning the speaker-to-microphone transfer function which allows us to perform the task unobtrusively. Using the obtained residual signal in the ear canal, we extract three features and deploy a machine-learning model for estimating the air leakage level. We investigated our system under various conditions to validate its robustness and resilience against the motion and other artefacts. Our extensive experimental evaluation shows that the proposed method can track air leakage levels under different daily activities. "The best computer is a quiet, invisible servant." ~Mark Weiser
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带入耳式麦克风的耳机的无干扰漏气估计
耳戴式设备(入耳式可穿戴设备)因其人体工学和非侵入性特点,在传感应用和医疗保健研究领域日益受到关注。然而,由于日常活动或佩戴的变化,设备和用户耳朵之间的空气泄漏会降低应用性能,干扰校准,并降低整个系统的鲁棒性。现有文献缺乏估算漏气程度(即密封完整性)的既定方法,无法为耳机应用提供信息。在这项工作中,我们提出了一种基于耳内麦克风估算耳塞漏气程度的新型非侵入式方法。该方法旨在通过学习扬声器到麦克风的传递函数来估算耳道中失真、反射和外部噪音的大小,同时排除扬声器的输出,从而使我们能够不露痕迹地完成任务。利用获得的耳道残余信号,我们提取了三个特征,并部署了一个机器学习模型来估计漏气水平。我们在各种条件下研究了我们的系统,以验证其对运动和其他伪影的鲁棒性和复原力。广泛的实验评估表明,所提出的方法可以在不同的日常活动中跟踪漏气水平。"最好的计算机是一个安静的隐形仆人"。~马克-韦泽
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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