'Automatic Ingestion Monitor Version 2' - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images

• Main Authors: Doulah, A. (Author), Ghosh, T. (Author), Hossain, D. (Author), Imtiaz, M.H (Author), Sazonov, E. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: algorithm; and food imagery; Article; body mass; caloric intake; Continuous captures; cross-sectional study; Data Collection; dietary assessment; Dietary assessments; eating; Eating; electronic device; energy intake; food; Food; Food consumption; food intake; Food intake detection; Food intake detections; Food supply; human; Humans; image analysis; imagery; information processing; Mean and standard deviations; Monitoring, Physiologic; Nutrition; nutritional assessment; observational study; physical activity; physiologic monitoring; Privacy by design; Privacy concerns; questionnaire; sedentary lifestyle; support vector machine; Wearable cameras; Wearable devices; Wearable Electronic Devices; wearable sensors; Wearable sensors
• Online Access: View Fulltext in Publisher

 Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. 'Active' methods rely on the user to take an image of each eating episode. 'Passive' methods use wearable cameras that continuously capture images. Most of 'passively' captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor, AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) F1-score of 81.8 ± 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1-7. Continuous capture had concern value of 5.0 ± 1.6 (concerned) while image capture only during food intake had concern value of 1.9 ±1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users. © 2013 IEEE.