1. Real-time gesture transmission with a robotic hand : embodied signals for non-verbal remote communicationLea Pajnič, Matjaž Kljun, Anuradhi Maheshya W. Weerasinghe Arachchillage, Klen Čopič Pucihar, 2025, published scientific conference contribution Abstract: This work explores how computer vision and robotics can support remote, gesture-based embodied signals for expressing presence and emotion in remote communication. We present an initial proof-of-concept in which users interact through robotic hands placed on their desks: one user’s hand gestures are captured in real time by a camera, transmitted over a network, and reproduced by a robotic hand at the remote location. The prototype uses the InMoov robotic hand and MediaPipe Hands for gesture tracking across varied lighting conditions, viewing angles, and backgrounds. Our preliminary tests demonstrate that gestures can be reliably recognised and consistently reproduced through stable network communication. While still at an early stage, this project illustrates the potential of combining affordable robotics with computer vision to create accessible alternatives to voice communication and new forms of remote communication.
Keywords: robotic hand, gesture transmission, embodied signals, non-verbal communication, remote communication, computer vision
Published in RUP: 30.01.2026; Views: 781; Downloads: 5
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2. Enhanced precision in axle configuration inference for bridge weigh-in-motion systems using computer vision and deep learningDomen Šoberl, Jan Kalin, Andrej Anžlin, Maja Kreslin, Klen Čopič Pucihar, Matjaž Kljun, Doron Hekič, Aleš Žnidarič, 2025, original scientific article Abstract: Heavy goods vehicles (HGVs) have a significant impact on road and bridge infrastructure, with overloaded vehicles accelerating structural deterioration and increasing safety risks. Bridge weigh-in-motion (B-WIM) systems estimate gross vehicle weight (GVW) using strain measurements, but inaccuracies in axle configuration recognition can reduce reliability. This study presents a low-cost computer vision (CV) extension for existing B-WIM installations that verifies strain-inferred axle configurations using traffic camera images and flags GVW estimates as reliable or unreliable. Experiments on a data set of over 30,000 HGV records show that by combining convolutional neural networks with strain-based heuristics, GVW reliability can improve from 96.7% to 99.89%, effectively excluding nearly all erroneous measurements. The approach operates without interrupting ongoing B-WIM operations and can be applied retrospectively to historical data. Limitations include the inability to detect raised axles (RAs), which the method excludes as unreliable. This method provides a practical, high-precision enhancement for structural health monitoring of bridges.
Keywords: B-WIM, computer vision, deep learning
Published in RUP: 16.01.2026; Views: 780; Downloads: 5
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