Lehre

How do we transform physical energy into meaningful experience? How does the brain decide what to attend to, what to ignore, and what to remember? And why does all of this matter when we design technology for human use?

This course provides a systematic introduction to the perceptual and cognitive foundations of human information processing – with a consistent focus on their relevance for Human-Computer Interaction and interactive system design. The lecture traces the journey of information from sensation to memory: we explore how the visual, auditory, and somatosensory systems construct our experience of the world, how the brain integrates information across the senses, and how attentional and memory processes shape what we ultimately perceive, retain, and act upon. Along the way, classical psychophysical laws, perceptual illusions, and multisensory phenomena illustrate both the remarkable capabilities and the systematic limitations of human cognition.

The accompanying exercise equips students with the practical toolkit of empirical research. From reading and presenting scientific papers to experimental design, research ethics, and statistical analysis – students progressively build the skills needed to ask and answer research questions empirically. These competencies come together in a group mini-project.

This course will be conducted in english.

What makes a product easy to use — and how do we know? How can we move beyond intuition and systematically measure whether an interface actually works for its users?

This course introduces the core concepts, methods, and processes of usability engineering and testing — with a strong emphasis on quantitative, evidence-based approaches. Students learn how to plan and conduct controlled usability experiments, collect and analyze behavioral and subjective data, and derive actionable design recommendations from empirical findings. The course covers the full usability engineering lifecycle: from early-stage inspection methods and heuristic evaluation through formal experimental testing to summative benchmarking — always grounded in measurable outcomes such as task performance, error rates, efficiency, and user satisfaction.

The accompanying exercise puts these methods into practice on real-world open source projects. Rather than working with artificial toy examples, student teams select an existing open source application, systematically evaluate its usability using the methods introduced in the lecture, and propose and test design improvements. This means students engage with real codebases, real users, and real design constraints — experiencing firsthand that usability engineering is not an abstract academic exercise but a practical discipline that measurably improves the software people actually use.

This course will be conducted in english and in the summer term of 2027 for the first time.

What can a heartbeat reveal about cognitive load? Can a change in skin conductance tell a system that its user is frustrated? And what happens when technology doesn't just measure the body — but responds to it in real time?

This course explores the use of physiological signals as a channel of communication between humans and computers. We frame the field around three core desires: the desire to measure and understand what a person is experiencing, the desire to offer control and communication through bodily signals, and the desire to build systems that adapt and respond to a user's physiological state without explicit input. These three ambitions — observation, intention, and closed-loop adaptation — structure the course from the first session to the last.

The lecture begins with the body as a signal source: how and why the nervous system produces measurable electrical, mechanical, and optical signals, and what we can — and cannot — reliably infer from them. We then systematically work through the major signal modalities: cardiac activity and heart rate variability (ECG/HRV), electrodermal activity (EDA), brain signals (EEG), gaze and pupillometry, voice and speech as biosignals, and motion and respiration (IMU). For each, we cover physiology, signal processing, feature extraction, practical measurement trade-offs, and common pitfalls. The course broadens into the wider landscape of physiological sensing (fMRI, fNIRS, EMG), examines physiological synchrony as a window into social and collaborative processes, and closes with a critical discussion of ethics, privacy, and responsible design in a field where the data is inherently intimate.

The exercise is designed as a progressive research journey. Students begin by synthesizing and presenting scientific literature on a physiological computing theme, building domain knowledge and scientific communication skills. A dedicated methods and skills track then introduces experimental design for physiological studies, hands-on sensor workshops with real hardware (ECG chest straps, EDA sensors, eye trackers, smartphone-based sensing), and guided data processing pipelines — from raw signal to interpretable features. These competencies converge in a group research project: students develop a research question, design and pilot a study, collect and analyze physiological data, and present their findings — experiencing the full empirical cycle from question to conclusion.

This course will be conducted in english.