Recognizing Eating from Body-Worn Sensors
Recognizing Eating from Body-Worn Sensors
复制标题
通过穿戴式传感器识别饮食
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Samantha Kleinberg
中科院分区：
文献类型：
--
作者：
Mark Mirtchouk;D. Lustig;Alexandra Smith;Ivan Ching;Min Zheng;Samantha Kleinberg
许多应用都需要自动化的饮食监测解决方案，这些解决方案可以找出个人何时摄入、摄入什么以及摄入了多少，例如为患有慢性病的个人提供反馈。人体佩戴传感器的进步导致了高精度的系统来寻找食物，甚至每一口都消耗了哪些食物。然而，大多数测试都是在受控的实验室环境中进行的，饮食选择有限，背景噪音很小，受试者专注于吃东西。对于要被用户采用的这些系统，关键是它们在实际情况下能够很好地工作，并且能够处理背景噪音、共享餐饮和多任务等混杂因素。在现实环境中工作的准确性通常较低，但在确定基本事实方面存在挑战。最关键的是，实验室和自由生活环境之间存在着巨大的差距。通常在每种情况下为不同的人收集数据，这使得很难确定如何缩小精度差距，这加剧了这一问题。我们提出了一项关于进食识别的多通道研究，使用身体佩戴的运动(头部、手腕)和音频(耳塞麦克风)传感器对12名参与者(6名来自实验室研究，6名新测试泛化能力)进行了研究。与仅有音频具有最高精确度的实验室相比，我们发现现在需要组合感知模式(音频、运动)；但传感器位置(头部与手腕)并不关键。我们进一步发现，实验室数据确实适用于其他参与者，但尽管个人自由生活数据提高了准确性，但来自其他人的更多数据实际上可能会导致更差的表现。
Automated dietary monitoring solutions that can find when, what, and how much individuals consume are needed for many applications such as providing feedback to individuals with chronic disease. Advances in body-worn sensors have led to systems with high accuracy for finding meals and even which foods are consumed in each bite. However, most tests are done in controlled lab settings with restricted meal choices, little background noise, and subjects focused on eating. For these systems to be adopted by users it is critical that they work well in realistic situations and be able to handle confounding factors such as background noise, shared meals, and multi-tasking. Work in realistic environments usually has lower accuracy, but has challenges in determining ground truth. Most critically, there has been a significant gap between lab and free-living environments. This is compounded by data usually being collected for different individuals in each setting, making it difficult to determine how the accuracy gap can be closed. We present a multi-modality study on eating recognition, using body-worn motion (head, wrists) and audio (earbud microphone) sensors for 12 participants (6 from the lab study, 6 new to test generalizability). In contrast to the lab, where audio alone has the highest accuracy, we find now that a combination of sensing modalities (audio, motion) is needed; yet sensor placement (head vs. wrist) is not critical. We further find that lab data does generalize to other participants, but while personal free-living data improves accuracy, more data from others can actually lead to worse performance.
影响因子：
--
作者：
Burke LE;Wang J;Sevick MA
通讯作者：
Sevick MA