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雷达卡
摘要翻译：
本文描述了一项研究，以测试一种方法的准确性，跟踪手腕的运动在吃，以检测和计数咬。目的是评估其在人口（年龄、性别、种族）和咬（用具、容器、手用、食物类型）变量中的准确性。数据是在一个自助餐厅在正常饮食条件下收集的。共有271名参与者在吃一顿饭的同时戴着一个类似手表的设备来跟踪他们的手腕运动。同时记录了每个参与者的视频，并随后进行了审查，以确定咬伤的地面真实时间。咬人时间在操作上被定义为食物或饮料被放入嘴里的时刻。食物和饮料的选择没有脚本或限制。参与者2-4人一组就座，并被鼓励自然进食。总共吃了24,088口374种不同的食物和饮料。总体而言，自动检测咬伤的灵敏度为75%，阳性预测值为89%。在人口统计变量中发现了62-86%的敏感性，较慢的进食率倾向于较高的敏感性。食物类型对敏感性的影响与咬人时手腕的总运动有一定的相关性，可能是由于某些食物类型的人头部向盘子的运动增加，而手向嘴的运动减少。总的来说，这些发现提供了迄今为止最大的证据，表明该方法产生了一种可靠的自动测量无限制进食过程中摄入量的方法。
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英文标题：
《Assessing the Accuracy of a Wrist Motion Tracking Method for Counting
Bites across Demographic and Food Variables》
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作者：
Yiru Shen, James Salley, Eric Muth, Adam Hoover
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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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.
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PDF链接：
https://arxiv.org/pdf/1806.05352
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