Papapanagiotou Vasileios, Diou Christos, Langlet Billy, Ioakimidis Ioannis, Delopoulos Anastasios
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:7853-6. doi: 10.1109/EMBC.2015.7320212.
Monitoring and modification of eating behaviour through continuous meal weight measurements has been successfully applied in clinical practice to treat obesity and eating disorders. For this purpose, the Mandometer, a plate scale, along with video recordings of subjects during the course of single meals, has been used to assist clinicians in measuring relevant food intake parameters. In this work, we present a novel algorithm for automatically constructing a subject's food intake curve using only the Mandometer weight measurements. This eliminates the need for direct clinical observation or video recordings, thus significantly reducing the manual effort required for analysis. The proposed algorithm aims at identifying specific meal related events (e.g. bites, food additions, artifacts), by applying an adaptive pre-processing stage using Delta coefficients, followed by event detection based on a parametric Probabilistic Context-Free Grammar on the derivative of the recorded sequence. Experimental results on a dataset of 114 meals from individuals suffering from obesity or eating disorders, as well as from individuals with normal BMI, demonstrate the effectiveness of the proposed approach.
通过连续测量进餐重量来监测和改变饮食行为已成功应用于临床实践中，用于治疗肥胖症和饮食失调。为此，曼多计（一种盘秤）以及受试者单次进餐过程中的视频记录，已被用于协助临床医生测量相关的食物摄入量参数。在这项工作中，我们提出了一种新颖的算法，仅使用曼多计的重量测量数据就能自动构建受试者的食物摄入量曲线。这消除了直接临床观察或视频记录的需求，从而显著减少了分析所需的人工工作量。所提出的算法旨在通过使用Delta系数应用自适应预处理阶段，随后基于记录序列导数上的参数概率上下文无关文法进行事件检测，来识别特定的进餐相关事件（例如咬一口、添加食物、伪影）。对来自肥胖症或饮食失调患者以及BMI正常个体的114次进餐数据集的实验结果证明了所提方法的有效性。
