EAT: A Reliable Eating Assessment Technology for Free-living Individuals.

项目摘要/摘要 暴饮暴食和不健康的饮食往往与各种健康风险状况有关，如肥胖、高 血压，以及一些慢性病。为了更好地理解暴饮暴食和不健康饮食， 研究人员经常依赖个人提供的自我报告。关于改变生活方式的建议通常是 根据这些自我报告的观察结果提供的资料。然而，众所周知，自我报告可以是 错误的，容易受到报道偏见的影响。因此，客观地测量进食活动和验证 自我报告是必要的。最近，人们越来越有兴趣超越自我报告和 自动监控进食活动。为了自动、实时地监测，研究人员已经观察到 在使用来自手腕佩戴设备、脖子佩戴设备或耳朵佩戴设备的传感器数据来自动检测 吃东西。这些设备通常能够捕捉进食时间。然而，这些设备很少捕获 图像，从而限制了从视觉上确认所消费的食物及其数量的可能性。 随着可穿戴式相机的日益普及，捕捉进食正在逐渐成为可能 活动和相关联的上下文自动进行，无需任何用户干预。机器学习的研究进展 能够从这些捕获的图像中自动提取与饮食相关的信息。然而，可穿戴设备 摄像机通常会捕捉到更多不必要的信息，比如捕捉旁观者。这是不必要的 信息捕捉降低了参与者佩戴相机的意愿。目前，不存在任何摄像机 可以捕捉饮食活动，同时限制捕捉不必要的信息。混淆了 不必要的信息可能会增加参与者佩戴相机的意愿。然而，目前还不清楚是否 哪种模糊技术会增加参与者佩戴可穿戴相机的意愿，并在 同时确保自动确定上下文。在这个项目中，我们将确定使用 机器学习来检测视频中的饮食并识别模糊技术，该技术可以允许检测 在不收集不必要信息的情况下进行饮食活动。 为此，首先我们将开发一种活动检测算法，该算法将允许使用 来自红外传感器阵列和RGB图像的数据。接下来，我们将在交叉测试中测试各种混淆方法 根据参与者的最大可接受性，试验并选择最佳的混淆方法。然后我们将部署 在一种新型可穿戴式摄像头上采用最佳混淆方法的进食检测算法 红外传感器阵列。我们将使用这个摄像头来测试在现实世界中检测进食的可能性。至 验证我们的算法，我们将要求人们确认或驳斥预测的进食和不进食时刻。我们会 将该算法的性能与实时用户响应和24小时饮食召回进行比较 客观评价算法的性能。我们建议的系统将改进当前的研究实践 评估饮食摄入量，为行为医学的个性化干预铺平道路。

Project Summary/Abstract Overeating and unhealthy eating are often associated with various health risk conditions such as obesity, high blood pressure, and some chronic diseases. To get a better understanding of overeating and unhealthy eating, researchers often rely on self-reports provided by individuals. Suggestions regarding changing lifestyle is often provided based on observations from these self-reports. However, it is well known that self-reports can be erroneous and subject to reporting biases. Thus, an objective way to measure the eating activity and validating self-reports is necessary. Recently, there has been growing interest in moving beyond self-reports and monitoring the eating activity automatically. To monitor automatically, and in real time, researchers have looked at using sensor data from wrist worn devices, neck-worn devices, or ear-worn devices to automatically detect eating. These devices often enable capturing the eating periods. However, these devices seldom capture images, thus limiting the possibility of visually confirming the consumed food and their quantity. With the increasing popularity of wearable cameras, it is gradually becoming possible to capture the eating activities and associated context automatically and without any user intervention. Advances in machine learning enables automatically extracting eating related information from these captured images. However, wearable cameras often capture more information than necessary, like capturing bystanders. This unnecessary information capturing reduces participant's willingness to wearing the camera. Currently, no camera exists that can capture the eating activity and at the same time limit capturing unnecessary information. Obfuscating the unnecessary information might increase participant's willingness to wear the camera. However, it is unclear if and which obfuscation technique will increase participant's willingness to don the wearable camera and at the same time ensure automatic context determination. In this project, we will determine the possibility of using machine learning to detect eating in videos and identify the obfuscation technique that can allow detecting the eating activity without collecting unnecessary information. To this end, first we will develop an activity detection algorithm that will allow detecting the eating activity using data from an IR sensor array and RGB images. Next, we will test various obfuscation methods in a cross-over trial and select the best obfuscation method based on the greatest participant acceptability. We will then deploy the eating detection algorithm with the best obfuscation approach on a novel wearable camera that has an infrared sensor array. We will use this camera to test the possibility of detecting eating in a real-world setting. To validate our algorithm, we will ask people to confirm or refute predicted eating and non-eating moments. We will compare the performance of this algorithm against both real-time user response and 24-hour dietary recall to objectively evaluate the algorithm's performance. Our proposed system will improve current research practices of evaluating dietary intake and pave the way for personalized interventions for behavioral medicine.