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首页> 外文期刊>Sensors Journal, IEEE >AutoDietary: A Wearable Acoustic Sensor System for Food Intake Recognition in Daily Life
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AutoDietary: A Wearable Acoustic Sensor System for Food Intake Recognition in Daily Life

机译：AutoDietary：可穿戴式声学传感器系统，用于日常生活中的食物摄入识别

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摘要

Nutrition-related diseases are nowadays a main threat to human health and pose great challenges to medical care. A crucial step to solve the problems is to monitor the daily food intake of a person precisely and conveniently. For this purpose, we present AutoDietary, a wearable system to monitor and recognize food intakes in daily life. An embedded hardware prototype is developed to collect food intake sensor data, which is highlighted by a high-fidelity microphone worn on the subject’s neck to precisely record acoustic signals during eating in a noninvasive manner. The acoustic data are preprocessed and then sent to a smartphone via Bluetooth, where food types are recognized. In particular, we use hidden Markov models to identify chewing or swallowing events, which are then processed to extract their time/frequency-domain and nonlinear features. A lightweight decision-tree-based algorithm is adopted to recognize the type of food. We also developed an application on the smartphone, which aggregates the food intake recognition results in a user-friendly way and provides suggestions on healthier eating, such as better eating habits or nutrition balance. Experiments show that the accuracy of food-type recognition by AutoDietary is 84.9%, and those to classify liquid and solid food intakes are up to 97.6% and 99.7%, respectively. To evaluate real-life user experience, we conducted a survey, which collects rating from 53 participants on wear comfort and functionalities of AutoDietary. Results show that the current design is acceptable to most of the users.
机译：如今，与营养有关的疾病是对人类健康的主要威胁，对医疗保健构成了巨大挑战。解决问题的关键步骤是精确，方便地监控一个人的日常食物摄入量。为此，我们提出了AutoDietary，这是一个可穿戴系统，用于监视和识别日常生活中的食物摄入量。开发了一种嵌入式硬件原型来收集食物摄入传感器数据，佩戴在受试者脖子上的高保真麦克风可以突出显示该数据，从而以无创方式精确记录进餐期间的声音信号。声音数据经过预处理，然后通过蓝牙发送到智能手机，从而识别食物类型。特别是，我们使用隐马尔可夫模型来识别咀嚼或吞咽事件，然后对其进行处理以提取其时/频域和非线性特征。采用基于决策树的轻量级算法来识别食物的类型。我们还在智能手机上开发了一个应用程序，该应用程序以一种用户友好的方式汇总了食物摄入识别结果，并提供了关于健康饮食的建议，例如更好的饮食习惯或营养均衡。实验表明，AutoDietary对食物类型进行识别的准确度为84.9％，对液体和固体食物摄入量进行分类的准确率分别高达97.6％和99.7％。为了评估现实生活中的用户体验，我们进行了一项调查，该调查收集了53位参与者在佩戴舒适性和AutoDietary功能方面的评分。结果表明，当前的设计对大多数用户而言都是可以接受的。

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