A personalised, goal-driven, and explainable chatbot for nutritional coaching based on a multi-agent system
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2021
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Obesity and related sicknesses (e.g. type 2 diabetes) are global, ever-growing public health problems (Chooi, Ding and Magkos, 2019). In 2020, in particular, obesity had gained further significance since people who suffer from it have a higher likelihood of a severe disease progression after contracting SARS-CoV-2 (Zhou, Chi, Lv and Wang,n.d.). Nowadays, there are a lot of possible practices and tools to address obesity. Due to the proliferation of smartphones and other mobile devices and the wide availability of mobile internet access, many of these tools are now available in the form of smart phone apps or internet platforms. This thesis aims to develop a conversational program (so-called chatbot) that acts as a personalised nutrition coach. It offers its users the possibility to track their daily meals and snacks with an image recognition AI and keep track of their weight. This data is then made available to the user in the form of statistical analysis. Additional features include viewing healthy meal suggestions for all main meals and snacks and setting a goal (maintain or reduce weight), which affects the user’s daily calorie consumption calculated by the chatbot. This approach aims to raise awareness of the user’s daily food consumption. Which in turn should lead to a change in eating behaviour, so-called"mindful eating", which has been proven to lead to healthier eating behaviour according to studies by Jordan, Wang, Donatoni and Meier (2014) and Pintado-Cucarella andRodríguez-Salgado (2016).
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
EGGENSCHWILER, Stefan, 2021. A personalised, goal-driven, and explainable chatbot for nutritional coaching based on a multi-agent system. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/40333