Extending Fitts' Law to manual obstacle avoidance

Area of Science:

• Human-Computer Interaction
• Cognitive Psychology
• Robotics

Background:

• Fitts' Law accurately predicts movement times for direct point-to-point tasks.
• Predicting movement times for complex, obstacle-avoiding paths remains a challenge.

Purpose of the Study:

• To investigate the extension of Fitts' Law for predicting movement times (MTs) in obstacle-avoiding movements.
• To assess the impact of obstacle intrusion on movement time predictions.

Main Methods:

• Participants performed movements in a workspace with obstacles.
• Movement times were recorded and analyzed using an extended Fitts' Law model.
• The model incorporated movement index of difficulty and obstacle intrusion distance.

Main Results:

• The extended Fitts' Law model, including obstacle intrusion, significantly improved MT prediction accuracy.
• Predictions were more accurate than using the index of difficulty alone.
• The extended Fitts' Law performed comparably to models using actual movement paths.

Conclusions:

• Fitts' Law can be effectively extended to predict MTs for obstacle-avoiding movements.
• Workspace geometry, specifically obstacle intrusion, is a key factor in predicting movement times.
• This extension aligns with Fitts' Law's principle of using environmental factors for prediction.