Kursprogramm
Ubiquitous Computing (Ubicomp) is about computers that disappear into everyday objects, wearables and environments. The course teaches methods and techniques to design, build, analyse and evaluate ubiquitous computing systems – in particular information appliances and wearables – including their human-computer interaction and the evaluation with real users.
New in WS 26/27 – the complete development chain in one semester:
Idea space → Problem → Idea → Solution development → Implementation (enclosure, hardware, software) → Evaluation.
AI assistants, rapid fabrication (3D printing, laser cutting), low-cost PCB manufacturing and open hardware make it possible to take an appliance through this chain on your own. Understanding the problem and evaluating with real users cannot be delegated to AI – user studies remain essential.
Lectures:
1. Introduction and Organization – course overview, examples, skills and the development chain
2. Fundamentals and History – Weiser's vision, calm computing, physical and cognitive embedding, ParcTab, context sensitivity, wearable computing, living labs, reactive environments, Internet of Things
3. Technology Trends and Design – computing laws and technology trends, design models (Design Council, design thinking), information appliance design process, ethnographic studies and action research
4. Appliances and Wearables – definitions (Raskin, Norman), axioms of information appliances, smartwatches, glasses and earables, ergonomics, body maps, development process with case study
5. Fabrication – CAD and parametric modelling, 3D printing (FDM, SLA, SLS), laser cutting, CNC milling
6. Sensors, Recognition and Context – sensor types and MEMS, sampling and filtering, the recognition process from data collection to evaluation, context-aware systems
7. HCI Studies – study fundamentals, ubicomp field studies (think aloud, Wizard of Oz, diaries, experience sampling), study design and metrics, affordances, signifiers and mapping
8. PCB Design – PCB fundamentals and manufacturing, schematic and layout, components, soldering
9. Health Technologies – biopotentials (ECG, EEG, EMG, EOG), motion sensing and activity recognition, contactless sensing
10. Embedded Machine Learning – edge vs. cloud, network compression (quantization, pruning, distillation), human activity recognition, TinyHAR case study
11. Startup and Business Planning – Scrum, cost and effort models (COSYSMO), revenue streams, market research, Business Model Canvas, growth and scaling
The lecture is complemented by hands-on exercises in fabrication, hardware and user studies. Recommended literature per lecture is in the folder "Literature".
