Let's Grab a Drink: Teacher-Student Learning for Fluid Intake Monitoring using Smart Earphones
Let's Grab a Drink: Teacher-Student Learning for Fluid Intake Monitoring using Smart Earphones
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DOI：
10.1109/iotdi54339.2022.00014
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Shijia Zhang;Yilin Liu;Mahanth K. Gowda
中科院分区：
文献类型：
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作者：
Shijia Zhang;Yilin Liu;Mahanth K. Gowda
本文展示了使用耳机传感器估计液体摄入量的可行性，耳机传感器越来越受欢迎。液体消耗量估计在跟踪脱水和过度水合方面具有许多与医疗保健相关的应用，脱水和过度水合可与疲劳、易怒、高血压、肾结石等问题有关。因此，准确跟踪水合水平不仅对用户预防此类疾病有直接益处，而且还为医疗保健提供者提供诊断信息。为此，本文采用了一种拾音麦克风，它可以直接从皮肤接触和身体传导中捕获流体消耗过程中的身体振动。这导致提取更强的信号，同时不受周围环境噪声的影响。然而，准确估计的主要挑战是缺乏大规模训练数据集来训练机器学习模型（ML）。为了应对这一挑战，本文基于数据增强和半监督学习技术设计了鲁棒的ML模型。对12名用户进行的广泛用户研究显示，每次吞咽体积估计准确度为3.35 mL（误差为19.17%），整个瓶子的累积误差为3.26%，同时对身体运动、容器类型、液体温度、传感器位置等具有鲁棒性。ML模型在智能手机上实现，功耗和延迟低。
This paper shows the feasibility of fluid intake estimation using earphone sensors, which are gaining in popularity. Fluid consumption estimation has a number of healthcare-related applications in tracking dehydration and overhydration which can be connected to issues in fatigue, irritability, high blood pressure, kidney stones, etc. Therefore, accurate tracking of hydration levels not only has direct benefits to users in preventing such disorders but also offers diagnostic information to healthcare providers. Towards this end, this paper employs a voice pickup microphone that captures body vibrations during fluid consumption directly from skin contact and body conduction. This results in the extraction of stronger signals while being immune to ambient environmental noise. However, the main challenge for accurate estimation is the lack of availability of large-scale training datasets to train machine learning models (ML). To address the challenge, this paper designs robust ML models based on techniques in data augmentation and semi-supervised learning. Extensive user study with 12 users shows a per-swallow volume estimation accuracy of 3.35 mL (≈ 19.17% error) and a cumulative error of 3.26% over an entire bottle, while being robust to body motion, container type, liquid temperature, sensor position, etc. The ML models are implemented on smartphones with low power consumption and latency.