Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Tasks

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploits the generation of B-spline trajectories in the joint space and formulation of a multi-objective optimisation problem to online adjust the total execution time and smoothness of the robot trajectories. The former ensures human efficiency and productivity of the workplace, while the latter contributes to safeguarding the user's comfort and cognitive ergonomics. The performance of the proposed framework was evaluated in a typical industrial task. Results demonstrated its capability to enhance the productivity of the human-robot dyad while mitigating the cognitive workload induced in the worker

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences, encoding at the same time motion patterns and context. Additionally, the method introduces an event-based automatic video segmentation and clustering, which allows to group similar events, detecting also on the fly if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.Comment: 8 pages, 8 figures, submitted to IEEE RAS International Symposium on Robot and Human Interactive Communication (RO-MAN), for associated video see https://youtu.be/Ftu_EHAtH4

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences, encoding at the same time motion patterns and context. Additionally, the method introduces an event-based automatic video segmentation and clustering, which allows to group similar events, detecting also on the fly if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects

Maximising Coefficiency of Human-Robot Handovers through Reinforcement Learning

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may result in protracted and awkward robot motions, contributing to unpleasant sensations by the human partner and affecting perceived safety and social acceptance. This paper investigates whether transferring the cognitive science principle that “humans act coefficiently as a group” (i.e. simultaneously maximising the benefits of all agents involved) to human-robot cooperative tasks promotes a more seamless and natural interaction. Human-robot coefficiency is first modelled by identifying implicit indicators of human comfort and discomfort as well as calculating the robot energy consumption in performing the desired trajectory. We then present a reinforcement learning approach that uses the human-robot coefficiency score as reward to adapt and learn online the combination of robot interaction parameters that maximises such coefficiency . Results proved that by acting coefficiently the robot could meet the individual preferences of most subjects involved in the experiments, improve the human perceived comfort, and foster trust in the robotic partner

Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient's skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.Comment: Submitted to 2021 International Symposium on Medical Robotics (ISMR

An Online Framework for Cognitive Load Assessment in Industrial Tasks

The ongoing trend towards Industry 4.0 has revolutionised ordinary workplaces, profoundly changing the role played by humans in the production chain. Research on ergonomics in industrial settings mainly focuses on reducing the operator's physical fatigue and discomfort to improve throughput and avoid safety hazards. However, as the production complexity increases, the cognitive resources demand and mental workload could compromise the operator's performance and the efficiency of the shop floor workplace. State-of-the-art methods in cognitive science work offline and/or involve bulky equipment hardly deployable in industrial settings. This paper presents a novel method for online assessment of cognitive load in manufacturing, primarily assembly, by detecting patterns in human motion directly from the input images of a stereo camera. Head pose estimation and skeleton tracking are exploited to investigate the workers' attention and assess hyperactivity and unforeseen movements. Pilot experiments suggest that our factor assessment tool provides significant insights into workers' mental workload, even confirmed by correlations with physiological and performance measurements. According to data gathered in this study, a vision-based cognitive load assessment has the potential to be integrated into the development of mechatronic systems for improving cognitive ergonomics in manufacturing

Ergonomic human-robot collaboration in industry: A review

: In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces

Attitudes and Beliefs of the Italian Population towards COVID-19 Vaccinations

Background: Despite the numerous campaigns to encourage vaccination against COVID-19, the public debate and often conflicting information have left many individuals uncertain about the decision to make on whether or not to vaccinate. Methods: This research aims to analyze the attitudes and beliefs of the Italian population towards COVID-19 and other vaccinations through a quantitative methodology. In all, 500 adults (Age M = 39.52) participated in this exploratory study with an online questionnaire conducted in April 2021. Results: most participants believe vaccination is necessary to defeat COVID-19; there is an age-related difference in getting vaccinations, and women were more afraid of unexpected future effects than men; older participants have expressed a greater willingness to pay to be vaccinated against COVID-19 (4). Conclusion: In light of these results, it is necessary to pay greater attention to the perplexity and fears expressed by the population, especially women and youth, in relation to vaccinations; in fact, it would help to achieve a wider adherence to the tools designed to contain the spread of viruses at the base of severe health crises

Disentangling natural and anthropogenic influences on Patagonian pond water quality

The water quality of wetlands is governed not only by natural variability in hydrology and other factors, but also by anthropogenic activities. Patagonia is a vast sparsely-populated in which ponds are a key component of rural and urban landscapes because they provide several ecosystem services such as habitat for wildlife and watering for livestock. Integrating field-based and geospatial data of 109 ponds sampled across the region, we identified spatial trends and assessed the effects of anthropogenic and natural factors in pond water quality. The studied ponds were generally shallow, well oxygenated, with maximum nutrient values reported in sites used for livestock breeding. TN:TP ratio values were lower than 14 in > 90% of the ponds, indicating nitrogen limitation. Water conductivity decreased from de east to the west, meanwhile pH and dissolved oxygen varied associated with the latitude. To assess Patagonian ponds water status we recommend the measure of total suspended solids and total nitrogen in the water, and evaluate the mallín (wetland vegetation) coverage in a 100 m radius from the pond, since those features were significantly influenced by livestock land use. To evaluate the relative importance of natural variability and anthropogenic influences as driving factors of water quality we performed three generalized linear models (GLM) that encompassed the hydrology, hydroperiod and biome (to represent natural influences), and land use (to represent anthropogenic influences) as fixed effects. Our results revealed that at the Patagonian scale, ponds water quality would be strongly dependent on natural gradients. We synthetized spatial patterns of Patagonian pond water quality, and disentangled natural and anthropic factors finding that the dominant environmental influence is rainfall gradient.Fil: Epele, Luis Beltran. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel. Centro de Investigación Esquel de Montaña y Estepa Patagónica; ArgentinaFil: Manzo, Luz Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel. Centro de Investigación Esquel de Montaña y Estepa Patagónica; ArgentinaFil: Grech, Marta Gladys. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel. Centro de Investigación Esquel de Montaña y Estepa Patagónica; ArgentinaFil: Macchi, Pablo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación en Paleobiología y Geología; Argentina. Universidad Nacional de Río Negro. Sede Alto Valle. Instituto de Investigaciones en Paleobiología y Geología; ArgentinaFil: Claverie, Alfredo Ñancuche. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel; ArgentinaFil: Lagomarsino, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Miserendino, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel. Centro de Investigación Esquel de Montaña y Estepa Patagónica; Argentin