Quentin Lohmeyer

The Dilemma of Managing Iterations in Time-to-market Development Processes

This paper considers the authors’ experience in industrial practice and reviews it from the point of view of scientific discussion. From scientific point of view there are three major Research questions: Why are iterations conceived differently? What makes an iteration valuable or harmful? What are appropriate strategies to deal with iterations under time pressure? The Papers give answers on this major research question by showing different aspects of time-to-market development processes and the challenge of effectively handling iterations within them. One of the authors was development head of a business unit responsible for various development and innovation projects. The other, in his position as global process manager for research and development, was responsible for the design and improvement of development processes in the same company. Thus both views represent the conflicting aspect of process modelling and iteration, which is a key topic in scientific discussion

Automated interpretation of eye–hand coordination in mobile eye tracking recordings

Mobile eye tracking is beneficial for the analysis of human–machine interactions of tangible products, as it tracks the eye movements reliably in natural environments, and it allows for insights into human behaviour and the associated cognitive processes. However, current methods require a manual screening of the video footage, which is time-consuming and subjective. This work aims to automatically detect cognitive demanding phases in mobile eye tracking recordings. The approach presented combines the user’s perception (gaze) and action (hand) to isolate demanding interactions based upon a multi-modal feature level fusion. It was validated in a usability study of a 3D printer with 40 participants by comparing the usability problems found to a thorough manual analysis. The new approach detected 17 out of 19 problems, while the time for manual analyses was reduced by 63%. More than eye tracking alone, adding the information of the hand enriches the insights into human behaviour. The field of AI could significantly advance our approach by improving the hand-tracking through region proposal CNNs, by detecting the parts of a product and mapping the demanding interactions to these parts, or even by a fully automated end-to-end detection of demanding interactions via deep learning. This could set the basis for machines providing real-time assistance to the machine’s users in cases where they are struggling.

Skimming and Scrutinizing: Quantifying Two Basic Patterns of Visual Behavior in Design

In design research recently an increasing number of eye tracking experiments have been conducted. The evaluation of the recorded data usually bases on analyzing a set of individual scan paths. In order to gain more value from the data and thus, to better explain human behavior in design, the authors proposed a differentiation of two basic scan path patterns. The first, skimming, is applied when a person wants to get an overview of a visual stimulus, while the second, scrutinizing, indicates that a person tries to understand its details. Although these patterns of visual behavior describe basically different cognitive processes, their differentiation still bases on personal judgment. This paper presents the results of an investigation that aimed to quantify the patterns of skimming and scrutinizing. It introduces an algorithm that, based on numerical criteria, allows an automated detection of both skimming and scrutinizing sequences in eye tracking data.

The Integration of Quantitative Biometric Measures and Experimental Design Research

In design research, recently an increasing number of experiments have been conducted that successfully applied quantitative biometric measurement methods to investigate design-related research questions. These methods are heart rate variability (HRV), skin conductance response (SCR), electroencephalography (EEG), functional magnetic resonance imaging (fMRI) as well as remote and mobile eye tracking (ET). Within the scope of these experiments, a variety of different biometric measurement systems have been used, each able to record specific raw data and each using characteristic measures to detect and specify particular patterns of human behaviour. This chapter explores how these biometrical measurement systems work, what exactly they measure, and in which ways collected raw data can be analysed to obtain meaningful results. By using the example of selected design studies, the benefits as well as the limitation of the aforementioned biometric measurement methods are discussed and reflected in regard to their present and future role in experimental design research.