Mental stress and safety awareness during human-robot collaboration - Review

Comparison of proactive and reactive interaction modes in a mobile robotic telecare study

Mobile robotic telepresence systems require that information about the environment, the task, and the robot be presented to a remotely located user (operator) who controls the robot for a specific task. In this study, two interaction modes, proactive and reactive, that differ in the way the user receives information from the robot, were compared in an experimental system simulating a healthcare setting. The users controlled a mobile telepresence robot that delivered and received items (medication, food, or drink), and also obtained metrics (vital signs) from a simulated patient while the users performed a secondary healthcare-related task (they compiled health records which were displayed to them on the screen and answered related questions). The effect of the two interaction modes on overall performance and user perception was evaluated through a within-participant study design conducted with 50 participants belonging to two different types of populations (with and without a technological background). Efficiency, effectiveness, understanding, satisfaction, and situation awareness were defined as the dependent variables measured both objectively and subjectively. The proactive mode increased user performance, and understanding of the system and reduced the workload compared to the reactive mode. However, several of the users valued the option of increased user control experienced in the reactive mode. We, therefore, proposed design suggestions to highlight some of the benefits of factoring the reactive mode into the design as a hybrid mode.

Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing.

Human activity recognition in an end-of-life consumer electronics disassembly task

The production of electronic waste, also known as e-waste, has risen with the growing reliance on electronic products. To reduce negative environmental impact and achieve sustainable industrial processes, recovering and reusing products is crucial. Advances in AI and robotics can help in this effort by reducing workload for human workers and allowing them to stay away from hazardous materials. However, autonomous human motion/intention perception is a primary barrier in e-waste remanufacturing. To address the research gap, this study combined experimental data collection with deep learning models for accurate disassembly task recognition. Over 570,000 frames of motion data were collected from inertial measurement units (IMU) worn by 22 participants. A novel sequence-based correction (SBC) algorithm was also proposed to further improve the accuracy of the overall pipeline. Results showed that models (CNN, LSTM, and GoogLeNet) had an overall accuracy of 88-92%. The proposed SBC algorithm improved accuracy to 95%.