dc.contributor.author | Sharma, Aakash | |
dc.contributor.author | Czerwinska, Katja P | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Dagenborg, Håvard | |
dc.date.accessioned | 2023-05-08T07:57:01Z | |
dc.date.available | 2023-05-08T07:57:01Z | |
dc.date.issued | 2023-01 | |
dc.description.abstract | Nutritionplaysakeyroleinanathlete’s performance, health, and mental well-being. Capturing nutrition data is crucial for analyzing those relations and performing necessary interventions. Using traditional methods to capture long-term nutritional data requires intensive labor, and is prone to errors and biases. Artificial Intelligence (AI) methods can be used to remedy such problems by using Image-Based Dietary Assessment (IBDA) methods where athletes can take pictures of their food before consuming it. However, the current state of IBDA is not perfect. In this paper, we discuss the challenges faced in employing such methods to capture nutrition data. We also discuss ethical and legal issues that must be addressed before using these methods on a large scale. | en_US |
dc.description | Presentation at the 29th International Conference on Multimedia Modeling, 09. - 13.01.23, Bergen, Norway: <a href=https://www.mmm2023.no/>https://www.mmm2023.no/</a>. | en_US |
dc.identifier.cristinID | FRIDAID 2104335 | |
dc.identifier.uri | https://hdl.handle.net/10037/29154 | |
dc.language.iso | eng | en_US |
dc.relation.projectID | Norges forskningsråd: 274451 | en_US |
dc.relation.projectID | Norges forskningsråd: 263248 | en_US |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.title | Capturing Nutrition Data for Sports: Challenges and Ethical Issues | en_US |
dc.type.version | acceptedVersion | en_US |
dc.type | Conference object | en_US |
dc.type | Konferansebidrag | en_US |