用于食物摄入检测的耳道压力传感器

IF 1.9 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Frontiers in electronics Pub Date : 2023-07-18 DOI:10.3389/felec.2023.1173607
Delwar Hossain, Tonmoy Ghosh, Masudul Haider Imtiaz, E. Sazonov
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摘要

本文介绍了一种新型耳道压力传感器(ECPS),用于在受控和自由生活条件下客观检测食物摄入,咀嚼计数和食物图像捕获。本研究的贡献有三个方面:1)开发和验证一种新型可穿戴传感器,该传感器使用耳道压力变化和设备加速度作为食物摄入的指标;2)一种识别咀嚼段并计算每次进食过程中咀嚼次数的方法;3)通过传感器检测触发相机,促进仅在进食过程中以自我为中心的图像捕获,从而降低功耗、隐私问题以及存储和计算成本。方法:为了验证该装置,收集了10名志愿者在受控环境和3名志愿者在自由生活环境中的数据。在受控活动期间,每位参与者佩戴该设备约1小时,在自由生活期间佩戴该设备约12小时。在实验的两个部分中,参与者的食物摄入量都没有受到任何限制。训练独立于主题的支持向量机分类器,从压力传感器和加速度计的特征中识别食物摄入的时间段,以及仅从压力传感器中识别特征。结果:留一交叉验证的结果显示,在受控环境中,仅使用压力传感器特征的平均5秒分类f得分为87.6%,同时使用压力传感器和加速度传感器特征的平均5秒分类f得分为88.6%。对于自由生活的环境,两种分类器都能准确地检测到所有的进食事件。该可穿戴传感器在受控环境中使用压力传感器分类器,对来自进食视频的人工注释的咀嚼次数进行计数,准确率达到95.5%。讨论:人工审查图像发现,只有3.7%的捕获图像属于检测到的进食事件,这表明传感器触发的相机捕获可能有助于减少捕获图像的数量和传感器的功耗。
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Ear canal pressure sensor for food intake detection
Introduction: This paper presents a novel Ear Canal Pressure Sensor (ECPS) for objective detection of food intake, chew counting, and food image capture in both controlled and free-living conditions. The contribution of this study is threefold: 1) Development and validation of a novel wearable sensor that uses changes in ear canal pressure and the device’s acceleration as an indicator of food intake, 2) A method to identify chewing segments and count the number of chews in each eating episode, and 3) Facilitation of egocentric image capture only during eating by triggering camera from sensor detection thus reducing power consumption, privacy concerns, as well as storage and computational cost.Methods: To validate the device, data were collected from 10 volunteers in a controlled environment and three volunteers in a free-living environment. During the controlled activities, each participant wore the device for approximately 1 h, and during the free living for approximately 12 h. The food intake of the participants was not restricted in any way in both part of the experiment. Subject-independent Support Vector Machine classifiers were trained to identify periods of food intake from the features of both the pressure sensor and accelerometer, and features only from the pressure sensor.Results: Results from leave-one-out cross-validation showed an average 5 sec-epoch classification F-score of 87.6% using only pressure sensor features and 88.6% using features from both pressure sensor and accelerometer in the controlled environment. For the free-living environment, both classifiers accurately detected all eating episodes. The wearable sensor achieves 95.5% accuracy in counting the number of chews with respect to manual annotation from the videos of the eating episodes using a pressure sensor classifier in the controlled environment.Discussion: The manual review of the images found that only 3.7% of captured images belonged to the detected eating episodes, suggesting that sensor-triggered camera capture may facilitate reducing the number of captured images and power consumption of the sensor.
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