Optimal Feedback Control for Modeling Human-Computer Interaction

TL;DR

This paper applies optimal feedback control theory to model and analyze human pointing movements in HCI, proposing a unified dynamical system approach and providing a Python toolbox for researchers.

Contribution

It introduces a novel application of OFC to HCI, compares multiple models, and offers a new parameter identification procedure and software toolbox.

Findings

OFC models can replicate human pointing movements.

The proposed method accurately estimates user control parameters.

The Python toolbox facilitates HCI movement analysis and optimization.

Abstract

Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim to control their body optimally, within the constraints imposed by body, environment, and task. In this paper, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolbox OFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment by moment basis.