Squish This: Force Input on Soft Surfaces for Visual Targeting Tasks

Title of the Paper

Squish This: Force Input on Soft Surfaces for Visual Targeting Tasks

Paper Information

• Field: Human-Computer Interaction (HCI), Performance Evaluation of Force Input on Soft Surfaces
• Keywords: Soft surface, pressure input, force input, user study, visual targeting task

Research Background and Problem

• Identified Problems or Challenges: Most studies focus on the performance of pressure-based input on rigid surfaces, with limited attention to soft surfaces, resulting in insufficient understanding of force input performance on soft surfaces.
• Significance: The proliferation of computing devices has driven diverse interaction designs, particularly on soft surfaces (e.g., clothing, furniture, or human skin), which hold significant potential for human-computer interaction.
• Motivation and Related Work:
  • Existing research indicates that surface softness provides additional tactile cues through deformation, but its systematic impact on pressure input performance remains unclear.
  • The advantages of pressure input have been demonstrated on rigid surfaces, such as for single-handed control of slider values, accessing areas on mobile devices, or quickly selecting commands in linear menus.
  • Few studies have examined the performance of pressure input on soft surfaces, and those that do often lack systematic design or involve too few participants, making the results difficult to generalize.

Solution

• Proposed Solution: Designed and implemented a user experiment system using silicone samples with three levels of softness to systematically evaluate the impact of surface compliance on pressure input performance.
• Innovations:
  • First systematic evaluation of the effect of surface compliance on pressure input performance.
  • Ensured result reproducibility by linearizing sensor output, reducing bias from inherent sensor characteristics.
  • Improved accuracy and selection time in the experiment using the Quick Release method for optimized selection.
• Implementation Steps and Key Techniques:
  • Prepared three silicone samples with different softness levels (soft, medium, hard) and calibrated pressure sensors to achieve high linearity.
  • Participants controlled a linear slider with single-finger pressure and completed visual targeting tasks by selecting pressure targets.
  • Employed a multi-level randomized experimental design to evaluate participants' adaptability and performance differences across surfaces of varying softness.
  • Data analysis emphasized effect sizes and confidence intervals, de-emphasizing traditional p-value testing.

Research Findings

• Specific Findings:
  1. Performance:
     • Participants achieved an average accuracy of 95.42% in visual targeting tasks across the three soft surfaces, with surface compliance having minimal impact on accuracy.
     • Surface compliance significantly increased selection time and the number of crossings during the selection process at high pressure levels.
  2. User Preferences: Harder surfaces were the most preferred, but participants found softer surfaces more comfortable at low pressure levels.
  3. High-Pressure Selection Capability: The study was the first to discover that users could maintain high accuracy (94.7%) even under a dense 20-force-level condition, far exceeding the commonly reported capability level (typically 10±2 force levels) in previous research.
• Comparison with Existing Solutions:
  1. This study revealed that the capability of pressure input has been underestimated, with potential performance far exceeding what is reported in traditional literature.
  2. The proposed Quick Release selection mechanism reduced jitter effects and optimized performance.
• Experiment or Evaluation Results: The pressure range and selection mechanism used were key to high performance, with participants significantly outperforming earlier studies.
• Limitations and Future Directions:
  1. Limitations:
     • Focused only on single-finger pressure input, without exploring multi-finger interaction or non-linear compliance feedback.
     • Investigated linear visual mapping methods but did not delve into the effects of other mapping approaches.
  2. Future Directions:
     • Extend research to more complex materials, multi-finger interactions, and broader application scenarios.
     • Explore how non-linear visual mappings can optimize pressure input performance, especially in high-pressure selection tasks.

This study highlights the importance of sensor calibration and visual mapping in improving user experience when designing force input interfaces and calls for designers to recognize the potential of soft surface interactions.