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The course introduces core interaction techniques and the underlying technologies used to sense, interpret, and respond to user input in interactive systems. It covers the end-to-end pipeline from raw signals to interaction, including sensing modalities, sampling and noise, feature extraction, pattern recognition and machine learning approaches, sensor fusion, and feedback design with attention to real time constraints such as latency and robustness. Students work with contemporary toolchains and datasets in hands on exercises that emphasize practical implementation, experimentation, and evaluation.

After completing the module, students can analyze and design interaction techniques by reasoning about the full sensing to feedback pipeline and the tradeoffs between accuracy, responsiveness, usability, and technical complexity. They can implement and evaluate basic interaction components, collect and process time series data, choose appropriate signal processing and learning methods, and assess system behavior using suitable metrics and experimental procedures. Students can critically reflect on limitations, failure modes, and ethical implications of sensing and automated interpretation, and communicate design decisions and evaluation.

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