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Area of Science:

  • Human-Computer Interaction
  • Ubiquitous Computing
  • Machine Learning

Background:

  • On-body device position awareness is crucial for high-quality smartphone services.
  • Current systems support only pre-defined positions, misclassifying new ones.
  • This limitation hinders usability when users adopt novel device placements.

Purpose of the Study:

  • To propose a framework for discovering and incorporating previously unsupported on-body device positions.
  • To develop a method for identifying new position candidates from user data.
  • To reduce user burden in labeling and enable on-the-fly system adaptation.

Main Methods:

  • Utilized clustering, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), for grouping similar device positions.
  • Developed a novel method to determine the optimal 'eps' parameter for DBSCAN, enhancing cluster accuracy.
  • Proposed criteria for qualifying identified clusters as new, previously unrecognized device positions.

Main Results:

  • The proposed method effectively identifies new device positions without prior knowledge of their quantity.
  • Optimizing the DBSCAN 'eps' parameter significantly improves the accuracy of new position candidate identification.
  • The framework successfully prepares reliable datasets for on-the-fly retraining, reducing manual labeling efforts.

Conclusions:

  • The developed framework enables adaptive on-body device position awareness systems.
  • This approach enhances smartphone service quality by accommodating user-defined device placements.
  • The method offers a practical solution for discovering and integrating new positions, improving overall system robustness and user experience.