1. Gesture recognition on deformable objects using millimeter-wave radarNuwan Attygalle, Matjaž Kljun, Klen Čopič Pucihar, 2025, published scientific conference contribution Abstract: 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.
Keywords: gesture recognition, deformable objects, millimeter-wave radar
Published in RUP: 23.06.2025; Views: 168; Downloads: 6
 Full text (9,65 MB)
This document has more files! More... |
2. Assessing medical training skills via eye and head movementsKayhan Latifzadeh, Luis A. Leiva, Klen Čopič Pucihar, Matjaž Kljun, Iztok Devetak, Lili Steblovnik, 2025, published scientific conference contribution Abstract: We examined eye and head movements to gain insights into skill development in clinical settings. A total of 24 practitioners participated in simulated baby delivery training sessions. We calculated key metrics, including pupillary response rate, fixation duration, or angular velocity. Our findings indicate that eye and head tracking can effectively differentiate between trained and untrained practitioners, particularly during labor tasks. For example, head-related features achieved an F1 score of 0.85 and AUC of 0.86, whereas pupil-related features achieved F1 score of 0.77 and AUC of 0.85. The results lay the groundwork for computational models that support implicit skill assessment and training in clinical settings by using commodity eye-tracking glasses as a complementary device to more traditional evaluation methods such as subjective scores.
Keywords: eye movemens, head movements, simulation training
Published in RUP: 23.06.2025; Views: 179; Downloads: 8
Full text (3,50 MB)
This document has more files! More... |
3. |
4. |
5. |
6. |
7. |
8. |
9. |
10. |
