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Workshopband MuC 2024

https://dl.gi.de/handle/20.500.12116/44224
Auflistung nach:

Auflistung Workshopband MuC 2024 nach Autor:in "Amft, Oliver"

1 - 2 von 2
  • Workshop
    AI and Health: Using Digital Twins to Foster Healthy Behavior
    (Mensch und Computer 2024 - Workshopband, 2024) Keppel, Jonas; Ivezić, Dijana; Gruenefeld, Uwe; Lukowicz, Paul; Amft, Oliver; Schneegass, Stefan
    This workshop brings researchers together to discuss and explore how artificial intelligence (AI) can be used to improve general health. During our workshop at the MuC conference, we will focus on three main areas: developing ethical AI health recommendations, exploring how smart technologies in our homes can influence our health habits, and understanding how different types of feedback can change our health behaviors. The workshop aims to be a space where various research areas meet, encouraging a shared understanding and creating new ways to use AI to encourage healthy living. By focusing on real-world applications of AI and digital twins, we seek to guide our discussions toward strategies that have a direct and positive impact on individual and societal health.
  • Workshopbeitrag
    Patient Adherence and Challenges in a Weight Loss Study: Smartphone Data Stream and Gamification
    (Mensch und Computer 2024 - Workshopband, 2024) Ivezić, Dijana; Keppel, Jonas; Schneegass, Stefan; Amft, Oliver
    This paper presents findings from our implementation of a context aware health guidance system for obese individuals, with a focus on smartphone- and smartwatch-based health monitoring and participant adherence. To aid participants in weight loss, our system utilizes data from wearables and smartphones, integrating nutrition tracking and gamification elements into a smartphone application and a web-based health dashboard for health coaches. Eight participants completed a 120-day field study to evaluate the system and examine user adherence to health monitoring and the effectiveness of gamification in a weight loss program. Data on steps, sleep, heart rate, weather, and manually logged meals were collected. Adherence varied across data types, with step counts being the most consistently collected, while sleep and heart rate data were limited due to inconsistent smartwatch usage.