Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2025

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Human-Robot Interaction

Sortuj według:

Ogranicz wyniki do:

Human-robot collaboration in the workplace perception of technical and social science students in Poland

Litwinek Justyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2025

z. 216

253--263

Purpose: The article investigates the perception of human-robot collaboration (HRC) in the workplace among students with diverse fields of study (social and technical). The primary objective was to identify differences in attitudes, interests, and emotional responses towards robots, providing insights into their acceptance and future integration into professional environments. Design/methodology/approach: The research employed a survey-based approach, collecting data from 130 students: 69 from social sciences and 61 from technical fields using the CAWI technique. It focused on analysing students' interest in technology and science fiction, their associations and emotions linked to robots, preferences for robot appearance, and opinions on robot functionality in various contexts. Findings: The results show some statistically significant differences in the perception of robots and cooperation with robots in the workplace, depending on the field of study. Research limitations/implications: The study's limitations include its reliance on a survey method, small sample size, and differences in gender participation across the study's fields. Practical implications: The results suggest that the perceptions of both robots and collaboration in the workplace differ across the groups analysed. This indicates the need for tailored workplace strategies to reduce discomfort and enhance collaboration with robots. Social implications: The research highlights the importance of functional and user-friendly designs for robot designers and the importance of preparing students for future HRC scenarios through theoretical and practical experiences. Originality/value: This research sheds light on the connections between education, psychology, and robotics, delving into the constructs related to the perception and acceptance of robots in the workplace and contributing to a broader discourse on factors related to the acceptance and perception of technology by the generation entering the workforce.

An improved human pose estimation using Deep Neural Network for the optimization of human-robot interactions

Raj Ravi, Kos Andrzej

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 4

Research shows that mobile support robots are becoming increasingly valuable in various situations, such as monitoring daily activities, providing medical services, and supporting elderly people. For interpreting human conduct and intention, these robots largely depend on human activity recognition (HAR). However, previous awareness of human appearance (human recognition) and recognition of humans for monitoring (human surveillance) are necessary to enable HAR to work with assistance robots. Al-so However, multimodal human behavior recognition is constrained by costly hardware and a rigorous setting, making it challenging to effectively balance inference accuracy and system expense. Naturally, a key problem in human pose or behavior detection is the ability to extract additional purposeful interpretations from easily accessible live videos. In this paper, we employ human pose detection to address the problem and provide well-crafted assessment measures to show demonstrate the effectiveness of our approach, which utilizes deep neural networks (DNNs) This article proposes a human intention detection system that anticipates human intentions in human- and robot-centered scenarios by utilizing the incorporation of visual information as well as input features, including human positions, head orientations, and critical skeletal key points. Our goal is to aid human-robot interactions by helping mobile robots through real-time human pose prediction using the recognition of 18 distinct key points in the body's structure. The effectiveness of this strategy is demonstrated by the suggested study using Python, and the results of simulations verify the reliability and accuracy of this method.