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, objavljeni znanstveni prispevek na konferenci Opis: 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.
Ključne besede: robotic hand, gesture transmission, embodied signals, non-verbal communication, remote communication, computer vision
Objavljeno v RUP: 30.01.2026; Ogledov: 636; Prenosov: 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, izvirni znanstveni članek Opis: 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.
Ključne besede: B-WIM, computer vision, deep learning
Objavljeno v RUP: 16.01.2026; Ogledov: 649; Prenosov: 5
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10. The effect of vision elimination during quiet stance tasks with different feet positionsNejc Šarabon, Jernej Rošker, Stefan Loefler, Helmut Kern, 2013, izvirni znanstveni članek Opis: Literature confirms the effects of vision and stance on body sway and indicates possible interactions between the two. However, no attempts have been made to systematically compare the effect of vision on the different types of stance which are frequently used in clinical and research practice. The biomechanical changes that occur after changing shape and size of the support surface suggest possible sensory re-weighting might take place. The purpose of this study was to assess the effect of vision on body sway in relation to different stance configurations and width. Thirty-eight volunteers performed four quiet stance configurations (parallel, semi-tandem, tandem and single leg), repeating them with open and closed eyes. Traditional parameters, recurrence quantification analysis and sample entropy were analyzed from the CoP trajectory signal. Traditional and recurrence quantification analysis parameters were affected by vision removal and stance type. Exceptions were frequency of oscillation, entropy and trapping time. The most prominent effect of vision elimination on traditional parameters was observed for narrower stances. A significant interaction effect between vision removal and stance type was present for most of the parameters observed (p < 0.05). The interaction effect between medio-lateral and antero-posterior traditional parameters differed in linearity between stances. The results confirm the effect of vision removal on the body sway. However, for the medio-lateral traditional parameters, the effects did not increase linearly with the change in width and stance type. This suggests that removal of vision could be more effectively compensated by other sensory systems in semi-tandem stance, tandem and single legged stance
Ključne besede: balance, Body sway, Sensory manipulation, vision
Objavljeno v RUP: 15.10.2013; Ogledov: 5820; Prenosov: 155
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