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.
The objective of this paper is to present a mathematical tool for real-time tracking of whole-body compressive forces induced by external physical solicitations. This tool extends and enriches our recently introduced ergonomics monitoring system to asses the level of risk associated with human physical activities in human-robot collaboration contexts. The methods developed so far only considered the effect of the external loads on joint torque variations. However, even for negligible values of the joint torque overloadings (e.g., in singular configurations), the effect of compressive forces, defined by the internal/pushing forces among body links, can be significant. Accordingly, we propose the joint compressive forces as an additional real-time index for the assessment of human ergonomics. First, a simulation study is performed to validate the method. Then, follows a laboratory study on five subjects to compare the trend of the joint compressive forces with muscle activities. Results demonstrate the significance of the proposed index in the development of a comprehensive human ergonomics monitoring framework. Based on such a framework, robotic strategies as well as feedback interfaces can be employed to guide and optimise the human movement toward more convenient body configurations thus avoiding pain and consequent injuries.
The objective of this paper is to present a personalisable human ergonomics framework that integrates a method for real-time identification of a human model and an ergonomics monitoring function. The human model is based on a floating base structure and on a Statically Equivalent Serial Chain (SESC) model used for the estimation of the whole-body centre of Mass (CoM). A recursive linear regression algorithm (i.e., Kalman filter) is developed to achieve the online identification of the SESC parameters. A visual feedback provides a minimum set of suggested human poses to speed up the identification process, while enhancing the model accuracy based on a convergence value. The online ergonomics monitoring function computes and displays the overloading effects on body joints in heavy lifting tasks. The overloading joint torques are calculated based on the displacement of the Center of Pressure (CoP) between the measured one and the estimated one. Unlike our previous work, the entire process, from the model identification (personalisation) to ergonomics monitoring, is performed in real-time. We evaluated the efficacy of the proposed method through human experiments during model identification and load lifting tasks. Results demonstrate the high exploitation potential of the framework in industrial settings, due to its fast personalisation and ergonomics monitoring capacity.
Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO , an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multicamera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.
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