1. Radar-based gesture recognition on deformable objectsKlen Čopič Pucihar, Matjaž Kljun, Nuwan Attygalle, 2026, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: This chapter investigates the feasibility and challenges of using millimetre-wave radar for gesture recognition on deformable objects, such as plush toys or other objects made of flexible materials, which are typically not instrumented with sensors. Unlike vision-based systems, which are limited by occlusion and require clear line of sight, radar sensing can detect gestures through non-conductive materials. The authors compare prior work on gesture recognition performance across mid-air, on-object and on-deformable-object contexts using different radar signal representations and deep learning models. In addition, the authors conduct an experiment demonstrating that object deformations do not negatively impact recognition accuracy. These findings open new possibilities for contactless interaction with soft materials in everyday environments without requiring embedded instrumentation.
Ključne besede: radar, gesture recognition, deformable objects
Objavljeno v RUP: 18.05.2026; Ogledov: 237; Prenosov: 10
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2. Gesture recognition on deformable objects using millimeter-wave radarNuwan Attygalle, Matjaž Kljun, Klen Čopič Pucihar, 2025, objavljeni znanstveni prispevek na konferenci Opis: Although deformable objects are not typically designed for digital interaction, they offer a largely unexplored potential—any such object could be repurposed as a medium for controlling digital content. While existing approaches embed sensors into deformable objects to enable interaction, this limits scalability and practicality of such systems. An alternative is to perform gesture recognition on deformable objects using a wrist-worn radar sensor. However, when analysing reflected radar signals it is difficult to separate reflections originating from the continues deformations of the object shape and those from the user’s hand and fingers. Additionally, the continuous shape changes of deformable objects introduce changes in radar cross-section, affecting signal variability. Furthermore, user ergonomics—such as variations in hand size, finger dexterity, and strength—are likely to influence the degree of object deformation during interaction. In this paper, we explore whether radar sensing can be used for robust gesture detection on deformable objects, focusing on how well does a system generalize to previously unseen users and what can we do to improve such generalisability. In pursuit of this goal, we record a dataset of 4.3k labelled gestures with Google Soli millimeter-wave radar sensor on a plush toy and demonstrates robust classification performance, achieving accuracy of up to 90% on a five-gesture set. Furthermore, we investigate model generalizability and show that transfer learning improves recognition for previously unseen users, yielding performance gains of up to 20%. These findings highlight the potential of radar-based sensing for spontaneous and practical interaction with deformable objects.
Ključne besede: gesture recognition, deformable objects, millimeter-wave radar
Objavljeno v RUP: 23.06.2025; Ogledov: 1122; Prenosov: 11
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3. No interface, no problem : gesture recognition on physical objects using radar sensingNuwan Attygalle, Luis A. Leiva, Matjaž Kljun, Christian Sandor, Alexander Plopski, Hirokazu Kato, Klen Čopič Pucihar, 2021, izvirni znanstveni članek Ključne besede: radar sensing, gesture recognition, deep learning
Objavljeno v RUP: 18.10.2021; Ogledov: 3541; Prenosov: 26
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4. The missing interface : micro-gestures on augmented objectsKlen Čopič Pucihar, Christian Sandor, Matjaž Kljun, Wolfgang Huerst, Alexander Plopski, Takafumi Taketomi, Hirokazu Kato, Luis A. Leiva, 2019, objavljeni znanstveni prispevek na konferenci Ključne besede: augmented reality, Google Soli, millimeter-wave radar, micro-gesture recognition
Objavljeno v RUP: 22.05.2019; Ogledov: 4102; Prenosov: 126
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