A human-driven control architecture for promoting good mental health in collaborative robot scenarios

Nicora, Matteo Lavit; André, Elisabeth; Berkmans, Daniel; Carissoli, Claudia; D’Orazio, Tiziana; Fave, Antonella Delle; Gebhard, Patrick; Marani, Roberto; Mira, Robert Mihai; Negri, Luca; Nunnari, Fabrizio; Fernández, Alberto Peña; Scano, Alessandro; Reni, Gianluigi; Malosio, Matteo

doi:10.1109/ro-man50785.2021.9515315

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…Moreover, the researchers are encountering difficulties due to the multidimensional construct and high subjectivity of cognitive processing and the typical human attitude of being ashamed and concealing about psychological state. In Nicora et al (2021) , a human-driven control architecture including a CoBot and an interactive virtual Avatar is envisioned for promoting good mental health. Preliminary attempts of introducing affective robotics in industrial settings were presented by Landi et al (2018) ; Villani et al (2018b , 2020) .…”

Section: Ergonomics In Human-robot Collaborationmentioning

confidence: 99%

“…Despite the growing concern for employees and employers worldwide in work-related stress and psychosocial risks, our careful investigation identified Lagomarsino et al (2022a , c) ; Landi et al (2018) ; Messeri et al (2021) ; Nicora et al (2021) ; Villani et al (2018b , 2020) as the only studies which attempt to tackle the challenge of online assessing the cognitive demands and mitigating pressures at work.…”

Section: Ergonomics In Human-robot Collaborationmentioning

confidence: 99%

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Ergonomic human-robot collaboration in industry: A review

et al. 2023

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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.

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Section: Ergonomics In Human-robot Collaborationmentioning

confidence: 99%

Section: Ergonomics In Human-robot Collaborationmentioning

confidence: 99%

Ergonomic human-robot collaboration in industry: A review

et al. 2023

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“…Following this line, factors such as upper limb fatigue, strain and effort have been repeatedly measured and assessed for various purposes including, as a main target, the customization of working cells and the design of supportive devices that have effects on mental health protection, load reduction for multiple aims, including the design of supportive devices, [ 7 ] and improvements in ergonomics [ 8 ], in order to reduce work absenteeism [ 9 ] and increased well-being. These scenarios are frequently depicted in recent research projects, where several human factors are involved, including humans interaction with robots, physical and mental health monitoring in order to guarantee workers with improved working conditions and promoting workers’ good mental health, biomechanical parameters assessment [ 10 , 11 ], physiological measures [ 12 ], and ergonomics improvement [ 13 ]. However, since these practices have been adopted only recently, and not yet in a systematic way, we observed that often there is not a correspondence between tests made in laboratory and those carried out in working environments in real factories, leading to a gap between potential applications and those that are implemented in workplaces.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

et al. 2023

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Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

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“…In recent years, the need for trustworthy RGB-D sensors has increased importance in many fields, such as environment reconstruction for robotic applications [1][2][3], people tracking from healthcare systems [4,5], object recognition for manufacturing tasks [6][7][8], and gesture recognition for natural human-computer interfaces [9,10].…”

Section: Introductionmentioning

confidence: 99%

Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Romeo

Marani

Perri

et al. 2022

Sensors

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Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm.

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A human-driven control architecture for promoting good mental health in collaborative robot scenarios

Cited by 14 publications

References 27 publications

Ergonomic human-robot collaboration in industry: A review

Ergonomic human-robot collaboration in industry: A review

Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

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