Department of Human Media Interaction, University of Twente, 7522 NB Enschede, The Netherlands.
Department of Biomedical Signals and Systems, University of Twente, 7500 AE Enschede, The Netherlands.
Sensors (Basel). 2024 Jan 16;24(2):0. doi: 10.3390/s24020571.
In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.
在克服全球超重和肥胖问题方面，自动饮食监测 (ADM) 作为饮食实践的支持被引入。ADM 的目的是在自由生活环境中自动、连续、客观地测量食物摄入量的各个维度。这可以简化食物登记过程，从而克服频繁的记忆、低估和高估问题。在这项研究中，开发了一种饮食事件检测传感器系统，该系统包括一个戴在手腕上的智能手表，其中包含加速度计和陀螺仪，用于检测进食姿势；一个戴在下巴上的压电传感器，用于检测咀嚼；以及一个由两个戴在胸部和腹部周围的带组成的呼吸感应体积描记传感器，用于检测食物吞咽。这些传感器结合在一起，以确定集中在饮食周期不同步骤的传感器组合在多大程度上可以提高饮食事件分类结果。六名受试者在一个受控环境中进行了实验，其中包括进食和非进食事件。为每个传感测量计算了特征，以训练支持向量机模型。这导致进食姿势的 F1 得分为 0.82，咀嚼食物的 F1 得分为 0.94，吞咽食物的 F1 得分为 0.58。
