1. Efficient material selection for training occluded mmWave radar-based gesture recognisersNuwan Attygalle, Luis A. Leiva, Matjaž Kljun, Klen Čopič Pucihar, 2026, original scientific article Abstract: Radar-based gesture recognition has emerged as a promising approach for unobtrusive interaction. Unlike camera-based systems, radar sensors can detect gestures through opaque materials, enabling seamless embedding into various everyday objects. However, it remains unclear how to train models efficiently for robust gesture recognition through diverse materials. To investigate this, we collected a dataset of 17,520 gesture recordings performed through 73 everyday materials. By comparing several material-sampling and data-augmentation strategies, we found that a small carefully selected representative subset of training materials was sufficient to match performance of a classifier trained on the full material dataset. Our results showed that the models trained on 14 quota-sampled materials achieved accuracies of 95.8% and 91.2%, comparable to training on all 73 materials (96.8% and 91.6%) and significantly better than training without material data (66.8% and 65.8%). Among the evaluated sampling approaches, Quota sampling also provided the best overall trade-off between performance and practicality. In contrast, classifiers trained on augmented data performed worse than those trained on actual material-specific data. Taken together, these findings indicate that, for the tested sensor, gesture set, and material collection, carefully selected real-material data offer a practical route to reducing material-specific data collection in radar-based gesture recognition while preserving generalisation. Code, models, and data are available in the public repository, with additional details provided in the supplementary materials: https://gitlab.com/hicuplab/seeing-through.
Keywords: deep learning, training optimization for neural networks, millimetre-wave radar, gesture recognition, material selection, sampling, synthetic noise
Published in RUP: 19.06.2026; Views: 588; Downloads: 12
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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: 1672; Downloads: 18
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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, original scientific article Keywords: radar sensing, gesture recognition, deep learning
Published in RUP: 18.10.2021; Views: 4018; Downloads: 26
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4. The wearable radar : sensing gestures through fabricsLuis A. Leiva, Matjaž Kljun, Christian Sandor, Klen Čopič Pucihar, 2020, published scientific conference contribution Keywords: radar, wearables, fabrics, Soli, gestures, deep learning
Published in RUP: 06.05.2021; Views: 4075; Downloads: 36
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5. 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, published scientific conference contribution Keywords: augmented reality, Google Soli, millimeter-wave radar, micro-gesture recognition
Published in RUP: 22.05.2019; Views: 4589; Downloads: 126
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