Visual Computing Lab, CERTH-ITI, 57001 Thessaloniki, Greece.
Innovative Use of Mobile Phones to Promote Physical Activity and Nutrition across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, 14152 Stockholm, Sweden.
Nutrients. 2020 Jan 13;12(1):209. doi: 10.3390/nu12010209.
Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel "Rapid Automatic Bite Detection" (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen's kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
进食行为会对肥胖和饮食失调产生重要影响，并与之相关。进食行为通常通过自我报告的测量方法进行评估，尽管这些方法在可靠性方面存在局限性，但基于其易于收集和分析的特点而被广泛采用。一种更好且被广泛应用的替代方法是基于对进餐行为事件（例如咀嚼）的人工标注对进食进行客观分析。然而，这种方法耗时耗力，并且经常受到人为错误的影响，限制了其在大规模研究中的可扩展性和成本效益。为了解决这一问题，我们研发了一种新的“快速自动咀嚼检测”（RABiD）算法，该算法可以从视频中提取和处理骨骼特征，从而自动测量进餐时间和咀嚼次数。在这些设置中，RABiD 算法在算法和人工标注之间实现了近乎完美的一致性（Cohen's kappa κ = 0.894；F1 分数：0.948）。此外，我们还使用 RABiD 算法分析了一个独立的进食行为实验（18 名女性参与者；45 餐；三种不同的食物），结果表明算法和人工标注之间具有极好的相关性。分析结果表明，尽管食物种类（土豆泥 vs. 肉丸）发生了变化，但总进餐时间保持不变，而咀嚼次数显著减少。最后，通过对进餐进度的描述性分析发现，不同类型的食物会影响咀嚼频率，尽管整体咀嚼模式保持相似（RABiD 和手动标注的结果相同）。参与者在进餐开始和结束时会更频繁地咀嚼，但在中间阶段会慢一些。从方法学的角度来看，RABiD 为实验分析人类进食行为提供了一种有效的、全自动的替代人工标注的方法，在成本和所需时间方面仅为人工标注的一小部分，同时不会损失任何信息和数据的真实性。
Zotero 插件
