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Auflistung nach Schlagwort "health"

1 - 6 von 6
  • Workshopbeitrag
    Codesign ending up with n=1
    (Mensch und Computer 2022 - Workshopband, 2022) Mucha, Henrik
    is not a classic paper. It is a thought piece. In the tradition of the workshop it is a stream of consciousness on a topic relevant to the workshop. It has no references. It is utterly opinion from personal experience. It is meant to start a conversation. A conversation on incentives for participation when designing in knowledge-intensive domains such as healthcare.
  • Workshopbeitrag
    How can Small Data Sets be Clustered?
    (Mensch und Computer 2021 - Workshopband, 2021) Weigand, Anna Christina; Lange, Daniel; Rauschenberger, Maria
    In many areas, only small data sets are available and big data does not play a significant role, e.g., in Human-Centered Design research. In the context of machine learning analysis, results of small data sets can be biased due to single variables or missing values. Nevertheless, reliable and interpretable results are essential for determining further actions, such as, e.g., treatments in a health-related use case. In this paper, we explore machine learning clustering algorithms on the basis of a small, health-related (variance) data set about early dyslexia screening. Therefore, we selected three different clustering algorithms from different clustering methods: K-Means, HAC and DBSCAN. In our case, K-Means and HAC showed promising results, while DBSCAN did not deliver distinct results. Based on our experiences, we provide first proposals on how to handle small data set clustering and describe situations in which using Human- Centered Design methods can increase interpretability of machine learning clustering results. Our work represents a starting point for discussing the topic of clustering small data sets.
  • Zeitschriftenartikel
    How to Handle Health-Related Small Imbalanced Data in Machine Learning?
    (i-com: Vol. 19, No. 3, 2021) Rauschenberger, Maria; Baeza-Yates, Ricardo
    When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology, Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.
  • Workshopbeitrag
    Ninja Ride Supporting movement through a rhythm oriented Exergame
    (Mensch & Computer 2012: interaktiv informiert – allgegenwärtig und allumfassend!?, 2012) Schering, Sandra; Pelikan, Constantin
    In this paper we present the music and rhythm oriented Exergame Ninja Ride in which the player has to assume the role of a bicycling, newspaper delivering ninja. The game aims to increase training motivation of children by integrating training aspects with motion gaming and music synchronicity in both gameplay and movement. Bicycling movements on an ergometer and arm gestures in the real world are detected with the Blobo Ball peripheral. User-generated content, based on self supplied music, can be integrated to increase replayability and to allow using preferred music in training.
  • Workshopbeitrag
    Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning
    (Mensch und Computer 2020 - Workshopband, 2020) Rauschenberger, Maria; Baeza-Yates, Ricardo
    When discussing interpretable machine learning results, researchers need to compare results and reflect on reliable results, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in missing early screening of dyslexia or wrong prediction of cancer. We present nine criteria that help avoiding over-fitting and biased interpretation of results when having small imbalanced data related to health. We present a use case of early screening of dyslexia with an imbalanced data set using machine learning classification to explain design decisions and discuss issues for further research.
  • Konferenzbeitrag
    Wisdom of the IoT Crowd: Envisioning a Smart Home-based Nutritional Intake Monitoring System
    (Mensch und Computer 2021 - Tagungsband, 2021) Faltaous, Sarah; Janzon, Simon; Heger, Roman; Strauss, Marvin; Golkar, Pedram; Viefhaus, Matteo; Prochazka, Marvin; Gruenefeld, Uwe; Schneegass, Stefan
    Obesity and overweight are two factors linked to various health problems that lead to death in the long run. Technological advancements have granted the chance to create smart interventions. These interventions could be operated by the Internet of Things (IoT) that connects different smart home and wearable devices, providing a large pool of data. In this work, we use IoT with different technologies to present an exemplary nutrition monitoring intake system. This system integrates the input from various devices to understand the users’ behavior better and provide recommendations accordingly. Furthermore, we report on a preliminary evaluation through semi-structured interviews with six participants. Their feedback highlights the system’s opportunities and challenges.