Evaluation of Vision-Based Hand Tool Tracking Methods for Quality Assessment and Training in Human-Centered Industry 4.0

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man-machine interaction and guarantee workers' safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings.

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human-robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.