This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist.
Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.
This paper introduces a web-platform system that performs semi-automatic compute of several risk indexes, based on the considered evaluation method (e.g., RULA—Rapid Upper Limb Assessment, REBA—Rapid Entire Body Assessment, OCRA—OCcupational Repetitive Action) to support ergonomics risk estimation, and provides augmented analytics to proactively improve ergonomic risk monitoring based on the characteristics of workers (e.g., age, gender), working tasks, and environment. It implements a body detection system, marker-less and low cost, based on the use of RGB cameras, which exploits the open-source deep learning model CMU (Carnegie Mellon University), from the tf-pose-estimation project, assuring worker privacy and data protection, which has been already successfully assessed in standard laboratory conditions. The paper provides a full description of the proposed platform and reports the results of validation in a real industrial case study regarding a washing machine assembly line composed by 5 workstations. A total of 15 workers have been involved. Results suggest how the proposed system is able to significantly speed up the ergonomic assessment and to predict angles and perform a RULA and OCRA analysis, with an accuracy comparable to that obtainable from a manual analysis, even under the unpredictable conditions that can be found in a real working environment.
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