Neural Scoring of Logical Inferences from Data using Feedback

• Allmin Susaiyah ^[1] ; Aki Härmä ^[3] ; Ehud Reiter ^[2] ; Milan PetkoviĆ ^[1]
  1. [1] Eindhoven University of Technology

     Eindhoven University of Technology

     Países Bajos

     
  2. [2] University of Aberdeen

     University of Aberdeen

     Reino Unido

     
  3. [3] Philips Research, Eindhoven

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• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 6, Nº. 5, 2021, págs. 90-99
• Idioma: inglés
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• Resumen

  • Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data.