An Intelligent Food-Intake Monitoring System Using Wearable Sensors

Jindong Liu, Edward Johns, L. Atallah, C. Pettitt, Benny P. L. Lo, G. Frost, Guang-Zhong Yang
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引用次数: 109

Abstract

The prevalence of obesity worldwide presents a great challenge to existing healthcare systems. There is a general need for pervasive monitoring of the dietary behaviour of those who are at risk of co-morbidities. Currently, however, there is no accurate method of assessing the nutritional intake of people in their home environment. Traditional methods require subjects to manually respond to questionnaires for analysis, which is subjective, prone to errors, and difficult to ensure consistency and compliance. In this paper, we present a wearable sensor platform that autonomously provides detailed information regarding a subject's dietary habits. The sensor consists of a microphone and a camera and is worn discretely on the ear. Sound features are extracted in real-time and if a chewing activity is classified, the camera captures a video sequence for further analysis. From this sequence, a number of key frames are extracted to represent important episodes during the course of a meal. Results show a high classification rate of chewing activities, and the visual log demonstrates a detailed overview of the subject's food intake that is difficult to quantify from manually-acquired food records.
一种基于可穿戴传感器的智能摄食监测系统
肥胖在世界范围内的流行对现有的医疗保健系统提出了巨大的挑战。普遍需要对那些有合并症风险的人的饮食行为进行普遍监测。然而,目前还没有准确的方法来评估人们在家庭环境中的营养摄入量。传统方法需要受试者手动回复问卷进行分析,具有主观性,容易出错,且难以保证一致性和遵从性。在本文中,我们提出了一个可穿戴传感器平台,该平台可以自动提供有关受试者饮食习惯的详细信息。该传感器由一个麦克风和一个摄像头组成,并被分散地戴在耳朵上。声音特征被实时提取,如果咀嚼活动被分类,摄像头就会捕捉到视频序列以供进一步分析。从这个序列中,提取出一些关键帧来表示用餐过程中的重要情节。结果显示咀嚼活动的分类率很高,视觉日志显示了受试者食物摄入的详细概述,这很难从人工获取的食物记录中量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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