Radar-Based Human-Computer Interaction by Klen Čopič Pucihar

Radar-Based Human-Computer Interaction by Klen Čopič Pucihar

Radars, historically used in fields such as surveillance, aviation, and ground imaging, can provide new application opportunities in interactive computer systems. Their specific signal representations obscure human identity, making them well-suited for integration into privacy-oriented applications in our everyday lives. 

This book explores how radar enables robust, non-contact sensing for the detection of human presence, implementation of gesture-based interaction, and recognition of user intent without the need of physical contact. With scientific and technical contributions from experts in radar engineering, signal processing, human-computer interaction, computational modelling, and machine learning, the book presents signal processing techniques and tools, surveys publicly available datasets, and highlights novel applications integrating radar sensing.

Radar-Based Human–Computer Interaction grounds this emerging interdisciplinary field, addressing researchers and practitioners interested in building the next generation of robust, adaptive, and privacy-preserving user interfaces through radar integration.

Dr. Klen Čopič Pucihar is an Associate Professor at the University of Primorska, co-director of HICUP Lab, and a Research Fellow at Stellenbosch University. He holds a PhD from Lancaster University and specializes in radar sensing, user modeling, mixed reality, and memory augmentation using generative tools. His research combines user studies with diverse sensing modalities—such as radar, eye tracking, and facial expression analysis—with principles of computational thinking, including abstraction, automation, and analysis, to design, explain, and build future interactive systems. He has received awards at top conferences including ACM IUI, IEEE ISMAR, and ACM ISS, and contributes to the community by organizing scientific events, such as the First Workshop on Radar-Based HCI at EICS 2024 and multi-year service on program committees for IEEE ISMAR, ACM IUI, and ACM UMAP.

Radu-Daniel Vatavu is a Professor of Computer Science at the Ștefan cel Mare University of Suceava, Romania, where he leads the Machine Intelligence and Information Visualization Lab (MintViz). With a PhD in Computer Science, his work focuses on human-computer interaction, ambient intelligence, extended reality, and accessible computing. Prof. Vatavu has led or co-led multiple funded research projects on topics involving gesture, multimodal, and natural interaction, and his research has received paper awards at CHI, ICMI, IMX, and EICS.

Dr. Dariush Salami is a Radio Research Scientist at Nokia Bell Labs and a Researcher at Aalto University, Finland. He earned his Ph.D. as a Marie Skłodowska-Curie Fellow in the European Training Network WindMill, specializing in AI/ML for wireless communication and sensing. His work focuses on the intersection of cellular networks, radar technologies, machine learning, and human-computer interaction, with an emphasis on mmWave sensing for gesture recognition, localization, and human-centric applications. Dr. Salami has received the Aalto University Thesis Award and multiple prestigious grants, including from the Nokia Foundation, Startup Säätiö, FUUG Foundation, HPY Research Foundation, and TES. Committed to open science, he has released numerous datasets and tools to advance research in radar-based sensing and interaction.

Dr. Nuwan T. Attygalle is a postdoc fellow at Université Catholique de Louvain, and a researcher in human-computer interaction specializing in gesture sensing through materials using mm-wave radar, recently completing his PhD at the University of Primorska. His interdisciplinary work bridges signal processing, machine learning, and HCI, with contributions spanning radar sensing, augmented reality-based language learning, protein structure analysis, and generative models for handwriting synthesis. He has received an Honourable Mention at ACM EICS 2022 and best paper award at ACM IUI 2025.