Abstract
The study of human factors is fundamental for the human-centered design of Smart Workplaces. IIoT (Industrial Internet of Things) technologies, mainly wearable devices, are becoming necessary to acquire data, whose analysis will be used to make decision in a smart way. For industrial applications, motion-tracking systems are strongly developing, being not invasive and able to acquire high amounts of data related to human motion in order to evaluate the ergonomic indexes in an objective way, as well as suggested by standards. For these reasons, a modular inertial motion capture system has been developed at the Department of Engineering of the University of Campania Luigi Vanvitelli. By using low cost Inertial Measurement Units – IMU and sensor fusion algorithms based on Extended Kalman filtering, the system is able to estimate the orientation of each body segment, the posture angles trends and the gait recognition during a working activity in industrial environment. From acquired data it is possible to develop further algorithms to online asses ergonomic indexes according to methods suggested by international standards (i.e. EAWS, OCRA, OWAS). In this paper, the overall ergonomic assessment tool is presented, with an extensive result campaign in automotive assembly lines of Fiat Chrysler Automobiles to prove the effectiveness of the system in an industrial scenario.
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References
Veltink, P., Bussmann, H., de Vries, W., Martens, W., van Lummel, R.: Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehabil. Eng. 4, 375–385 (1996)
Lyons, G., Culhane, K., Hilton, D., Grace, P., Lyons, D.: A description of an accelerometer-based mobility monitoring technique. Med. Eng. Phys. 27, 497–504 (2005)
Mayagoitia, R., Nene, A., Veltink, P.: Accelerometer and rate gyroscope measurement of the kinematics: an inexpensive alternative to optical motion analysis systems. J. Biomech. 35, 537–542 (2002)
Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C., Robert, P.: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 50, 711–723 (2003)
Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors (2009)
Zhou, H., Stone, T., Hu, H., Harris, N.: Use of multiple wearable inertial sensors in upper limb motion tracking. Med. Eng. Phys. 30(1), 123–133 (2008)
Yun, X., Bachmann, E.R.: Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans. Robot. 22(6), 1216–1227 (2006)
Caputo, F., Greco, A., D’Amato, E., Notaro, I., Spada, S.: A preventive ergonomic approach based on virtual and immersive reality. In: Advances in Intelligent Systems and Computing, Proceedings of the AHFE 2017 International Conference on Ergonomics in Design, Los Angeles, CA, USA (2017)
Caputo, F., Greco, A., D’Amato, E., Notaro, I., Spada, S.: Human posture tracking system for industrial process design and assessment. In: Advances in Intelligent Systems and Computing, Proceedings of the IHSI 2018 International Conference on Intelligent Human Systems Interaction, Dubai, vol. 722 (2018)
Acknowledgments
The authors would like to acknowledge the FCA – Fiat Chrysler Automobiles, EMEA Manufacturing Planning & Control – Ergonomics, and the LinUp S.r.l. for supporting the research work on which this paper is based.
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Caputo, F., Greco, A., D‘Amato, E., Notaro, I., Spada, S. (2019). IMU-Based Motion Capture Wearable System for Ergonomic Assessment in Industrial Environment. In: Ahram, T. (eds) Advances in Human Factors in Wearable Technologies and Game Design. AHFE 2018. Advances in Intelligent Systems and Computing, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-94619-1_21
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DOI: https://doi.org/10.1007/978-3-319-94619-1_21
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