A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing
A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing
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DOI：
10.1109/jsen.2011.2172411
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发表时间：
2012-05-01
影响因子：
4.3
通讯作者：
Fontana, Juan M.
中科院分区：
文献类型：
--
作者：
Sazonov, Edward S.;Fontana, Juan M.
客观和自动传感器系统监测个人的摄食行为出现作为一个潜在的解决方案，以取代不准确的自我报告方法。本文介绍了一种简单的传感器系统以及相关的信号处理和模式识别方法，用于基于咀嚼的无创监测来检测食物摄入的时间。一个压电应变计传感器被用来捕捉20名志愿者在安静地坐着、说话和进食时下颌的运动。这些信号被分割成固定长度的不重叠的epoch，并处理为每个epoch提取一组250个时间和频域特征。采用前向特征选择程序来选择最相关的特征，从4到11个特征中识别出对食物摄入检测最关键的特征。训练支持向量机分类器来创建食物摄入检测模型。20次交叉验证表明，每历元的分类精度为80.98%，时间分辨率为30 s。咀嚼应变传感器的简单性可能会导致一种更少干扰和更简单的方法来检测食物摄入量。所提出的方法可能会导致可穿戴传感器系统的发展，以评估个人的饮食行为。
Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption. These signals were segmented into non-overlapping epochs of fixed length and processed to extract a set of 250 time and frequency domain features for each epoch. A forward feature selection procedure was implemented to choose the most relevant features, identifying from 4 to 11 features most critical for food intake detection. Support vector machine classifiers were trained to create food intake detection models. Twenty-fold cross-validation demonstrated per-epoch classification accuracy of 80.98% and a fine time resolution of 30 s. The simplicity of the chewing strain sensor may result in a less intrusive and simpler way to detect food intake. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals.