Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
作者：
Highlights：
• An unsupervised model is proposed for the discovery of social and nutritional traits.
• Our proposed modular system allows an individual or joint applicability and analysis.
• Social-eating metrics help to quantify the camera wearer’s nutritional behaviour.
• Deep learning models proved to properly embed the behaviour information over time.
• Egocentric visual data exhibits to be a powerful resource for behaviour understanding.
摘要
•An unsupervised model is proposed for the discovery of social and nutritional traits.•Our proposed modular system allows an individual or joint applicability and analysis.•Social-eating metrics help to quantify the camera wearer’s nutritional behaviour.•Deep learning models proved to properly embed the behaviour information over time.•Egocentric visual data exhibits to be a powerful resource for behaviour understanding.
论文关键词：Pattern discovery,Egocentric vision,Nutrition,Behaviour understanding,Lifelogging,Deep learning
论文评审过程：Received 28 September 2020, Revised 24 November 2020, Accepted 14 December 2020, Available online 24 December 2020, Version of Record 20 January 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114506
