Intermittent control as a model of mouse movements

Martin, Juul; Gollee, H.; Müller, Jörg; Murray-Smith, Roderick

doi:10.48550/arxiv.2103.08558

Cited by 2 publications

(14 citation statements)

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“…One of the simplest models of mouse pointer dynamics is the second-order lag, which has been used as a baseline in several papers, including [83,91]. We therefore also include it as a baseline.…”

Section: Pointing As a Dynamical System: The Second-order Lagmentioning

confidence: 99%

“…Since 2OL-Eq, which we use as a baseline for the deterministic optimal control models, is not capable of predicting movement variability, we need another, stochastic baseline model. We decided to use a model from Intermittent Control (IC) theory, which recently has been proposed by Martin et al [83]. In the following, we will give a short overview of the similarities and differences between IC and OFC.…”

Section: Intermittent Control Model (Ic)mentioning

confidence: 99%

“…Traditionally, the field of Human-Computer Interaction (HCI) has concentrated on models such as Fitts' Law [37,38], predicting summary statistics of the movement such as movement time. Recently, more attention has been paid to modeling the underlying process by which the end-effector is controlled, predicting not only movement time, but end-effector position, velocity, and acceleration sequences, as well as applied forces (e.g., [32,36,83,91]).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, stochastic models such as LQG are able to fit the distribution of entire trajectories, given a specific user and task condition. Moreover, the fitting is significantly better than using the recently proposed Intermittent Control (IC) model [83] with respect to both KL divergence [71] and the 2−Wasserstein distance [96] serving as evaluation metrics. The considered deterministic OFC model, which does not take into account any noise terms, is able to predict average user behavior, given a slightly modified cost function.…”

Section: Introductionmentioning

confidence: 99%

“…In Sections 8 and 9, we extend this framework to the general stochastic case by adding different sensory-input models along with Gaussian motor and sensory noise (thus denoted as Linear-Quadratic Gaussian Regulator). We compare the stochastic OFC models to a recently proposed Intermittent Control model [83], which is briefly described in Section 10.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Feedback Control for Modeling Human-Computer Interaction

Fischer,

Fleig,

Klar

et al. 2021

Preprint

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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. We propose that in this case, the dynamics of the human body and computer can be interpreted as a single dynamical system. The state of this system 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, and show how these parameters can be used to gain insights regarding user characteristics and preferences. 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.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Section: Pointing As a Dynamical System: The Second-order Lagmentioning

confidence: 99%

Section: Intermittent Control Model (Ic)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Feedback Control for Modeling Human-Computer Interaction

Fischer,

Fleig,

Klar

et al. 2021

Preprint

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Simulating Interaction Movements via Model Predictive Control

Klar¹,

Fischer²,

Fleig³

et al. 2022

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We present a method to simulate movement in interaction with computers, using Model Predictive Control (MPC). The method starts from understanding interaction from an Optimal Feedback Control (OFC) perspective. We assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. In contrast to previous linear approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows us to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulated trajectories against user movements in a Fitts' Law type pointing study with four different interaction techniques. Our results show that the combination of distance, control, and joint acceleration cost matches individual users' movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers, we introduce CFAT, a novel method to identify maximum voluntary torques in joint-actuated models based on experimental data, and give practical advice on how to simulate human movement for different users, interaction techniques, and tasks.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Intermittent control as a model of mouse movements

Cited by 2 publications

References 0 publications

Optimal Feedback Control for Modeling Human-Computer Interaction

Optimal Feedback Control for Modeling Human-Computer Interaction

Simulating Interaction Movements via Model Predictive Control

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