Supporting food choices in the Internet of People: Automatic detection of diet-related activities and display of real-time interventions via mixed reality headsets

Open access
Date
2020-12Type
- Journal Article
Abstract
With the emergence of the Internet of People (IoP) and its user-centric applications, novel solutions to the many issues facing today's societies are to be expected. These problems include unhealthy diets, with obesity and diet-related diseases reaching epidemic proportions. We argue that the proliferation of mixed reality (MR) headsets as next generation primary interfaces provides promising alternatives to contemporary digital solutions in the context of diet tracking and interventions. Concretely, we propose the use of MR headset-mounted cameras for computer vision (CV) based detection of diet-related activities and the consequential display of visual real-time interventions to support healthy food choices. We provide an integrative framework and results from a technical feasibility as well as an impact study conducted in a vending machine (VM) setting. We conclude that current neural networks already enable accurate food item detection in real-world environments. Moreover, our user study suggests that real-time interventions significantly improve beverage (reduction of sugar and energy intake) as well as food choices (reduction of saturated fat). We discuss the results, learnings, and limitations and provide an overview of further technology- and intervention-related avenues of research required by developing an MR-based user support system for healthy food choices. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000428303Publication status
publishedExternal links
Journal / series
Future Generation Computer SystemsVolume
Pages / Article No.
Publisher
ElsevierSubject
Internet of People; Diet-related interventions; Mixed reality; Consumer health; Personal Internet-based healthcare; Novel ubiquitous interaction mechanismsOrganisational unit
03681 - Fleisch, Elgar / Fleisch, Elgar
Related publications and datasets
References: https://doi.org/10.3929/ethz-b-000467836
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