Group's research directions

Control Theoretic Modelling Biomechanics Augmented & Virtual Reality Novel Devices

Control Theoretic Modelling

Control Theoretic Models of Pointing

Control Theoretic Models of Pointing

Control Theoretic Models of Pointing

Jörg Müller       Antti Oulasvirta     Roderick Murray-Smith


This paper presents an empirical comparison of four models from manual control theory on their ability to model targetting behaviour by human users using a mouse: McRuer's Crossover, Costello's Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts' law based approaches in HCI, with models providing representations and predictions of human pointing dynamics which can improve our understanding of pointing and inform design.



Control Theoretic Models of Pointing
Jörg Müller, Antti Oulasvirta, Roderick Murray-Smith

ACM Transactions on Computer-Human Interaction 2017
Pointing Dynamics Dataset: Processed Data

Data after preprocessing steps: filtering, resampling, segmentation.

(zip archive of .csv files, 217MB)
Pointing Dynamics Dataset: Models

Simulink Models.

(zip archive of Simulink .slx files, 219KB)
Pointing Dynamics Dataset: Task Performance Data

Indicators of task performance, such as errors and movement times.

(zip archive of .csv files, 496KB)
Pointing Dynamics Dataset: Raw Data

Raw dataset.

(zip archive of .csv files, 304MB)


Simulation tool:

Simulink official web-page




Jörg Müller
Aarhus University
joerg.mueller (at) acm (dot) org