A novel approach for food intake detection using electroglottography.
A novel approach for food intake detection using electroglottography.
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DOI：
10.1088/0967-3334/35/5/739
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发表时间：
2014-05
影响因子：
3.2
通讯作者：
Sazonov E
中科院分区：
文献类型：
--
作者：
Farooq M;Fontana JM;Sazonov E
许多监测饮食和食物摄入量的方法依赖于受试者自我报告其每日摄入量。这些方法是主观的，可能不准确，需要被更准确和客观的方法取代。本文提出了一种使用电声门描记器 (EGG) 设备客观、自动检测食物摄入量的新方法。三十名受试者参加了一项 4 次访问的实验，其中涉及食用具有自我选择内容的膳食。 EGG 装置捕获了吞咽过程中食物通过引起的整个喉部电阻抗的变化。为了将所提出的方法与成熟的声学方法的性能进行比较，使用喉部麦克风来监测吞咽声音。两个信号都被分割成 30 秒的非重叠时期，并进行处理以提取小波特征。使用人工神经网络训练独立于受试者的分类器，以根据小波特征识别食物摄入的时期。留一法交叉验证的结果显示，基于 EGG 的方法的平均每周期分类准确度为 90.1%，基于声学的方法为 83.1%，证明了使用 EGG 进行食物摄入检测的可行性。
Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an Electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a 4-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained using Artificial Neural Networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross-validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.
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影响因子：
3
作者：
Dong, Yujie;Hoover, Adam;Scisco, Jenna;Muth, Eric
通讯作者：
Muth, Eric
影响因子：
3.8
作者：
Lopez-Meyer, Paulo;Makeyev, Oleksandr;Schuckers, Stephanie;Melanson, Edward L.;Neuman, Michael R.;Sazonov, Edward
通讯作者：
Sazonov, Edward
影响因子：
10.2
作者：
HEIL, CE;WALNUT, DF
通讯作者：
WALNUT, DF
影响因子：
4.3
作者：
Sazonov, Edward S.;Fontana, Juan M.
通讯作者：
Fontana, Juan M.
影响因子：
--
作者：
Sun M;Fernstrom JD;Jia W;Hackworth SA;Yao N;Li Y;Li C;Fernstrom MH;Sclabassi RJ
通讯作者：
Sclabassi RJ