Volume 63, Issue 3 - Physiological Computing / Guest Editors: Thomas Kosch, Miriam Hassib, Host Editor: Andreas Butz

We present GazeWheels: a series of visual feedback methods for dwell-based gaze input in the form of a wheel that is filled gradually until target selection. We evaluate three variations: Resetting, Pause & Resume and Infinite GazeWheel, and study how dwell duration and visual feedback position (co-located vs remote) impact performance. Findings from a user study (N = 19) show that Infinite and Pause & Resume GazeWheels are error prone but significantly faster than Resetting GazeWheel even when including error correction time. We conclude with five design recommendations.

Motor skills are omnipresent in our daily lives. Humans seek to learn new skills or improve existing ones. In this work, we explore how the actuation of the human body can be used to augment motor skills. We present GeniePutt, which augments the human performance via electrical muscle stimulation (EMS). We conducted a user study in which we controlled the turning angle of the wrist through GeniePutt to increase participants’ accuracy in a mini-golf scenario. Our results indicate that the best accuracy can be achieved when human capabilities are combined with augmentation performed through EMS.

In an increasingly digital world, intelligent systems support us in accomplishing many everyday tasks. With the proliferation of affordable sensing devices, inferring user states from collected physiological data paves the way to tailor-made adaptation. While estimating a user’s abilities is technically possible, such proficiency assessments are rarely employed to benefit the user’s task reflection. In our work, we investigate how to model and design for proficiency estimation as part of context-aware systems. In this paper, we present the definition and conceptual architecture of proficiency-aware systems. The concept is not only applicable to current adaptive systems but provides a stepping stone for systems which actively aid in developing user proficiency during interaction.

This article gives a short overview of my dissertation, where new algorithms are given for two fundamental graph problems. We develop novel ways of using linear programming formulations, even exponential-sized ones, to extract structure from problem instances and to guide algorithms in making progress. The first part of the dissertation addresses a benchmark problem in combinatorial optimization: the asymmetric traveling salesman problem (ATSP). It consists in finding the shortest tour that visits all vertices of a given edge-weighted directed graph. A ρ -approximation algorithm for ATSP is one that runs in polynomial time and always produces a tour at most ρ times longer than the shortest tour. Finding such an algorithm with constant ρ had been a long-standing open problem. Here we give such an algorithm. The second part of the dissertation addresses the perfect matching problem. We have known since the 1980s that it has efficient parallel algorithms if the use of randomness is allowed. However, we do not know if randomness is necessary – that is, whether the matching problem is in the class NC . We show that it is in the class quasi-NC . That is, we give a deterministic parallel algorithm that runs in poly-logarithmic time on quasi-polynomially many processors.