评估手腕运动跟踪方法在人口统计学和食物变量中计算咬合的准确性
Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables
摘要：
本文描述了一项研究，以测试一种方法的准确性，跟踪手腕运动在进食过程中检测和计数叮咬。目的是评估其在人口统计学（年龄，性别，种族）和咬合（器具，容器，手使用，食物类型）变量中的准确性。在正常饮食条件下的自助餐厅收集数据。共有271名参与者吃了一顿饭，同时戴着一个类似手表的设备来跟踪他们的手腕运动。同时记录每个参与者的视频，并随后进行审查，以确定叮咬的真实时间。在操作上，咬口时间被定义为食物或饮料放入口中的时刻。食物和饮料的选择没有脚本或限制。参与者以2-4人为一组坐下，并被鼓励自然进食。总共消费了374种不同食物和饮料的24,088口。总的来说，自动检测叮咬的方法的灵敏度为75%，阳性预测值为89%。在人口统计学变量中发现62-86%的敏感性范围，进食速度越慢，敏感性越高。由于食物类型的敏感性的变化表现出适度的相关性与总的手腕运动在咬，可能是由于头部朝板运动的增加和减少手对嘴运动的一些食物类型。总的来说，这些发现提供了迄今为止最大的证据，证明该方法在无限制饮食期间产生了可靠的自动摄入量测量。
英文摘要：
This paper describes a study to test the accuracy of a method that tracks wrist motion during eating to detect and count bites. The purpose was to assess its accuracy across demographic (age, gender, ethnicity) and bite (utensil, container, hand used, food type) variables. Data were collected in a cafeteria under normal eating conditions. A total of 271 participants ate a single meal while wearing a watch-like device to track their wrist motion. Video was simultaneously recorded of each participant and subsequently reviewed to determine the ground truth times of bites. Bite times were operationally defined as the moment when food or beverage was placed into the mouth. Food and beverage choices were not scripted or restricted. Participants were seated in groups of 2-4 and were encouraged to eat naturally. A total of 24,088 bites of 374 different food and beverage items were consumed. Overall the method for automatically detecting bites had a sensitivity of 75% with a positive predictive value of 89%. A range of 62-86% sensitivity was found across demographic variables, with slower eating rates trending towards higher sensitivity. Variations in sensitivity due to food type showed a modest correlation with the total wrist motion during the bite, possibly due to an increase in head-towards-plate motion and decrease in hand-towards-mouth motion for some food types. Overall, the findings provide the largest evidence to date that the method produces a reliable automated measure of intake during unrestricted eating.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
