Screenless Interactive Tabletop Gaming with Capacitive Surface Sensing

Title of the Paper

Screenless Interactive Tabletop Gaming with Capacitive Surface Sensing

Paper Information

• Subject Area: Human-Computer Interaction (HCI), applications of augmented reality and electronic touch technology in tabletop gaming
• Keywords: tabletop gaming, capacitive sensing, tangible interaction, touchscreen, machine learning, 3D printing

Research Background and Problem

• Problem or Challenge: Although there have been numerous attempts to integrate technology with tabletop games, such as mixed reality and interactive surfaces, these often disrupt the immersive experience of physical gameplay. This is especially problematic for beginners who may find complex hardware challenging to operate. Most augmented technologies interfere with the core elements of the game while sacrificing tactile experiences.
• Importance: Enhancing the tabletop gaming experience while preserving its original tactile nature is of practical significance, particularly when improving game strategy and fluidity without hardware interference. This represents an important yet underexplored challenge in the field of human-computer interaction.
• Research Motivation: To minimize the intrusion of technology on the tabletop gaming experience, this study aims to design an interactive system that operates without a screen, preserving the original game components while enabling automatic scoring and strategy assistance.

Solution

• Proposed Method or Solution:
  • A screenless interactive tabletop gaming system for automatic scoring is proposed, utilizing a transparent capacitive sensing film and movable conductive markers to achieve piece recognition and movement detection.
  • The system fully retains the original game components, avoiding permanent modifications to the parts.
  • Machine learning (Support Vector Machine classifier) is employed to detect game actions, reducing player intervention.
• Innovations:
  1. The use of commercial capacitive sensing films and 3D-printed conductive markers (designed without adhesive requirements) effectively identifies game actions, achieving low-cost, non-invasive hardware integration.
  2. The screenless design reduces distractions caused by screens.
  3. The automatic scoring mechanism allows players to focus on game strategy, enhancing the overall gaming experience.
• Implementation Steps and Key Technologies:
  1. Transparent capacitive sensing films are used to capture capacitive images of player actions, detecting specific patterns generated by conductive markers.
  2. A Support Vector Machine classifier (SVC) is applied to identify game piece types and actions, recording game states in real-time.
  3. A user-friendly graphical interface is developed to display scores and review player movement errors.

Research Outcomes

• Specific Results:
  • Compared to traditional manual scoring, the automated system reduced game round times by an average of 30 seconds per round, and players adopted more efficient strategies when using the automated system.
  • The system achieved a classification accuracy of 85% for sensing data, with accuracy reaching 100% during certain phases (e.g., the photosynthesis phase).
  • The use of capacitive films and conductive markers demonstrated the feasibility of the low-cost design, with players expressing satisfaction with its flexibility and aesthetic design.
• Advantages:
  • The system significantly reduced players' cognitive load related to scoring and rule management, enhancing their ability to focus on core game strategies.
  • The screenless and non-intrusive design received unanimous positive feedback from participants.
  • The system design is adaptable to a variety of tabletop games, offering high portability.
• Experimental or Evaluation Results:
  • In comparative experiments involving 10 pairs of participants, the use of the automated system significantly improved participants' round efficiency and strategy selection.
  • Surveys revealed that 90% of participants appreciated the automatic scoring feature, particularly when learning new game rules.
• Limitations and Future Directions:
  1. The current system's detection accuracy (85%) needs further improvement to achieve fully autonomous operation.
  2. The system currently lacks support for complex board designs (e.g., stacked pieces), and future work should expand sensing capabilities.
  3. Long-term user learning and adaptation by expert players have not yet been studied, necessitating extended user experiments.
  4. The integration of complex technology with game aesthetics (e.g., the visual appeal of screen displays) may require further optimization in the future.