AdaptiController: VR-Enhanced Fine Motor Assistance Through Finger Pressure Modulation.

Area of Science:

• Human-Computer Interaction
• Virtual Reality Systems
• Input Device Technology

Background:

• Conventional motion controllers in virtual reality (VR) face limitations in achieving high precision for delicate tasks.
• Existing kinematic metrics may not fully capture user intent for fine-grained control in VR environments.

Purpose of the Study:

• To investigate finger pressure as a continuous implicit input modality for enhancing interaction precision in virtual reality.
• To determine if finger pressure signals from VR controllers offer advantages over traditional kinematic metrics for precision tasks.

Main Methods:

• Empirical studies were conducted to establish a relationship between finger pressure dynamics and task precision requirements.
• A lightweight sigmoid-based model was developed to infer control granularity from detected finger pressure.
• Comparative evaluations were performed using video-scrubbing and VR sketching tasks.

Main Results:

• The adaptive pressure-based method demonstrated superior performance and subjective preference compared to static sensitivity baselines in video-scrubbing tasks.
• The technique maintained task performance while reducing mental demand in a VR sketching application.
• No significant increase in cognitive load was observed with the pressure-based input method.

Conclusions:

• Finger pressure is a viable and untapped input modality for improving precision in virtual reality interactions.
• Pressure-based input can effectively bridge the gap between coarse and fine-grained control in VR.
• This approach offers a pathway toward more versatile and intuitive VR input systems.