Designing for Feel: Contrasts between Human and Automated Parametric Capture of Knob Physics

Area of Science:

• Human-Computer Interaction
• Perception Science
• Mechanical Engineering

Background:

• The 'feel' of manual controls (knobs, sliders) is crucial for usability and safety in various applications.
• Objective physical-model identification methods aim to quantify control 'feel' for design support.
• Human perception of control dynamics is complex and context-dependent.

Purpose of the Study:

• To evaluate an objective physical-model identification method's ability to capture perceptually relevant parameters of manual controls.
• To compare the performance of the automated method against human identification of control 'feel'.
• To establish the method's effectiveness and limitations in capturing human-perceived dynamics.

Main Methods:

• Compared automated physical-model captures of five mechanical reference knobs with human (novice and expert) captures.
• Human participants adjusted parameters of a rendered knob model to match the feel of reference knobs.
• Assessed parameterizations for inertia, friction, detent strength, and detent spacing.

Main Results:

• Automated methods and human experts produced similar parameterizations for inertia, friction, detent strength, and spacing.
• Experts could better identify underlying models with unmodeled dynamics than novices.
• The objective algorithm outperformed all human participants when an appropriate physical model was used.

Conclusions:

• Automated model identification can effectively capture knob dynamics as perceived by humans.
• The study demonstrates the potential for objective methods to inform design guidelines for physical interfaces.
• Limitations exist, but the method represents a step towards computationally-informed design of tactile feedback.