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The increasing popularity of conducting studies in real-life settings, known as “studies in-the-wild”, is a valuable addition to the traditional controlled clinical trials. These studies enable the observation of long-term effects and account for the complex influences of everyday life. Body-worn sensors facilitate the continuous and unobtrusive collection of motion data in its user’s natural, everyday life environment. However, studies in-the-wild require careful planning regarding equipment usability, accessibility, and the creation of efficient study protocols to maximize the quality and output of the collected data. This paper presents insights from our recent study on compulsive handwashing, highlighting the challenges and strategies in study design, implementation, and label acquisition in order to perform supervised machine learning. We present approaches as well as the benefits and limitations of annotating data retrospectively so that participants are impacted minimally during the study. Finally, we list our learning and insights for upcoming studies of that kind.

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The Supervised Learning Dilemma: Lessons Learned from a Study in-the-Wild

  • 会议论文

  • 发表日期：

    2025年

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  • Kristina Kirsten,
  • Robin Burchard,
  • Olesya Bauer,
  • Marcel Miché,
  • Philipp Scholl,
  • Karina Wahl,
  • Roselind Lieb,
  • Kristof Van Laerhoven,
  • Bert Arnrich

摘要

The increasing popularity of conducting studies in real-life settings, known as “studies in-the-wild”, is a valuable addition to the traditional controlled clinical trials. These studies enable the observation of long-term effects and account for the complex influences of everyday life. Body-worn sensors facilitate the continuous and unobtrusive collection of motion data in its user’s natural, everyday life environment. However, studies in-the-wild require careful planning regarding equipment usability, accessibility, and the creation of efficient study protocols to maximize the quality and output of the collected data. This paper presents insights from our recent study on compulsive handwashing, highlighting the challenges and strategies in study design, implementation, and label acquisition in order to perform supervised machine learning. We present approaches as well as the benefits and limitations of annotating data retrospectively so that participants are impacted minimally during the study. Finally, we list our learning and insights for upcoming studies of that kind.