Biomechanical Models for Human-Computer Interaction

Abstract

Post-desktop user interfaces, such as smartphones, tablets, interactive tabletops, public displays and mid-air interfaces, already are a ubiquitous part of everyday human life, or have the potential to be. One of the key features of these interfaces is the reduced number or even absence of input movement constraints imposed by a device form-factor. This freedom is advantageous for users, allowing them to interact with computers using more natural limb movements; however, it is a source of 4 issues for research and design of post-desktop interfaces which make traditional analysis methods inefficient: the new movement space is orders of magnitude larger than the one analyzed for traditional desktops; the existing knowledge on post-desktop input methods is sparse and sporadic; the movement space is non-uniform with respect to performance; and traditional methods are ineffective or inefficient in tackling physical ergonomics pitfalls in post-desktop interfaces. These issues lead to the research problem of efficient assessment, analysis and design methods for high-throughput ergonomic post-desktop interfaces.

To solve this research problem and support researchers and designers, this thesis proposes efficient experiment- and model-based assessment methods for post-desktop user interfaces. We achieve this through the following contributions: - adopt optical motion capture and biomechanical simulation for HCI experiments as a versatile source of both performance and ergonomics data describing an input method; - identify applicability limits of the method for a range of HCI tasks; - validate the method outputs against ground truth recordings in typical HCI setting; - demonstrate the added value of the method in analysis of performance and ergonomics of touchscreen devices; and - summarize performance and ergonomics of a movement space through a clustering of physiological data.

The proposed method successfully deals with the 4 above-mentioned issues of post-desktop input. The efficiency of the methods makes it possible to effectively tackle the issue of large post-desktop movement spaces both at early design stages (through a generic model of a movement space) as well as at later design stages (through user studies). The method provides rich data on physical ergonomics (joint angles and moments, muscle forces and activations, energy expenditure and fatigue), making it possible to solve the issue of ergonomics pitfalls. Additionally, the method provides performance data (speed, accuracy and throughput) which can be related to the physiological data to solve the issue of non-uniformity of movement space. In our adaptation the method does not require experimenters to have specialized expertise, thus making it accessible to a wide range of researchers and designers and contributing towards the solution of the issue of post-desktop knowledge sparsity.