A Human Postures Inertial Tracking System for Ergonomic Assessments

  • Francesco Caputo
  • Alessandro Greco
  • Egidio D’Amato
  • Immacolata Notaro
  • Marco Lo Sardo
  • Stefania Spada
  • Lidia Ghibaudo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 825)


Since the early development for health purposes in 1950s, motion tracking systems have been strongly developed for several applications. Nowadays, using Micro Electro-Mechanics Systems (MEMS) technologies, these systems have become compact and light, being popular for several applications. Looking at the manufacturing industry, such as the automotive one, ergonomic postural analyses are a key step in the workplaces design and motion tracking systems represent fundamental tools to provide data about postures of workers while carrying out working tasks, in order to assess the critical issues according to ISO 11226 standard.

The aim of this work is to present an experimental wearable inertial motion tracking system, developed at the Dept. of Engineering of the University of Campania “Luigi Vanvitelli” in collaboration with Linup S.r.l., composed by several low-cost inertial measurement units (IMU).

The system allows to estimate the orientation of selected human body segments and to analyze the postures assumed during the working tasks. To increase the flexibility of use, the system is highly modular: it’s composed by 4 independent modules in full-body configuration, each one made of 3 or 4 inertial units.

In this paper, the overall system is presented, supported by several test cases, carried out in Fiat Chrysler Automobile (FCA) assembly lines, to test the system reliability in industrial environments. Furthermore, an automatic posture analysis code is presented to evaluate the postural critical issue of the workplaces.


Wearable devices Inertial measurement unit Industrial environment Working postures 



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.


  1. 1.
    I. 11226:2000 (2000) Ergonomics -Evaluation of Static Working PosturesGoogle Scholar
  2. 2.
    Ahmad N, Ghazilla RAR, Khairi NM, Kasi V (2013) Reviews on various inertial measurement unit (IMU) sensor application. Int J Signal Proc Syst 1:256–262CrossRefGoogle Scholar
  3. 3.
    Buke A, Gaoli F, Yongcai W, Lei S, Zhiqi Y (2015) Healthcare algorithms by wearable inertial sensors: a survey. China Commun 12:1–12CrossRefGoogle Scholar
  4. 4.
    Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Inf Fusion 35:68–80CrossRefGoogle Scholar
  5. 5.
    Caputo F, Greco A, D’Amato E, Notaro I, Spada S (2017) A preventive ergonomic approach based on virtual and immersive reality. In: Advanced in Ergonomics in Design: Proceedings of the AHFE 2017, Los AngelesGoogle Scholar
  6. 6.
    Caputo F, Greco A, D’Amato E, Notaro I, Spada S (2018) Human posture tracking system for industrial process design and assessment. In: Advances in Intelligent Systems and Computing, Proocedings of the IHSI 2018 International Conference on Intelligent Human Systems Interaction, vol 722, DubaiGoogle Scholar
  7. 7.
    Schaub K, Caragnano G, Britzke B, Bruder R (2012) The European Assembly Worksheet. Theor Issues Ergon Sci 14(6):1–23Google Scholar
  8. 8.
    International MTM Directorate, EAWS - manuale applicatoreGoogle Scholar
  9. 9.
    Dejnabadi H, Jolles BM, Aminian K (2005) A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes. IEEE Trans Biomed Eng 52(8):1478–1484CrossRefGoogle Scholar
  10. 10.
    Hyde R, Ketteringham L, Neild S, Jones R (2008) Estimation of upperlimb orientation based on accelerometer and gyroscope measurements. IEEE Trans Biomed Eng 55(2):746–754CrossRefGoogle Scholar
  11. 11.
    Yun X, Bachmann E (2006) Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans Robot 22(6):1216–1227CrossRefGoogle Scholar
  12. 12.
    Roetenberg D, Luinge H, Slycke P (2009) Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors, pp 1–7Google Scholar
  13. 13.
    Foxlin E, Harrington M (2002) Weartrack: a self-referenced head and hand tracker for wearable computers and portable VR. In: Proceedings of Fourth International Symposium on Wearable Computers, pp 155–162Google Scholar
  14. 14.
    Boonstra M, van der Slikke R, Keijsers N, van Lummel R, de Waal Malefijt M, Verdonschot N (2006) The accuracy of measuring the kinematics of rising from a chair with accelerometers and gyroscopes. J Biomech 39:354–358CrossRefGoogle Scholar
  15. 15.
    Lyons G, Culhane K, Hilton D, Grace P, Lyons D (2005) A description of an accelerometer-based mobility monitoring technique. Med Eng Phys 27:497–504CrossRefGoogle Scholar
  16. 16.
    Mayagoitia R, Nene A, Veltink P (2002) Accelerometer and rate gyroscope measurement of the kinematics: an inexpensive alternative to optical motion analysis systems. J Biomech 35:537–542CrossRefGoogle Scholar
  17. 17.
    Najafi B, Aminian K, Paraschiv-Ionescu A, Loew F, Bula C, Robert P (2003) Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Biomed Eng 50:711–723CrossRefGoogle Scholar
  18. 18.
    Honegger D, Meier L, Tanskanen P, Pollefeys M (2013) An open source and open hardware embedded metric optical flow cmos camera for indoor and outdoor applications. In: 2013 IEEE International Conference on Robotics and Automation, pp 1736–1741Google Scholar
  19. 19.
    Benini A, Mancini A, Longhi S (2008) An IMU/UWB/vision-based extended kalman filter for mini-UAV localization in indoor environment using 802.15.4a wireless sensor network. J Intell Robot Syst 24(5):1143–1156. Scholar
  20. 20.
    Mirzaei FM, Roumeliotis SI (2008) A kalman filter-based algorithm for imu-camera calibration: observability analysis and performance evaluation. IEEE Trans Robot 24(5):1143–1156CrossRefGoogle Scholar
  21. 21.
    Nützi G, Weiss S, Scaramuzza D, Siegwart R (2011) Fusion of IMU and vision for absolute scale estimation in monocular slam. J Intell Robot Syst 61(1):287–299. Scholar
  22. 22.
    D’Amato E, Mattei M, Mele A, Notaro I, Scordamaglia V (2017) Fault tolerant low cost IMUS for UAVS. In: 2017 IEEE International Workshop on Measurement and Networking (M N), pp 1–6Google Scholar
  23. 23.
    Yongliang W, Tianmiao W, JianHong L et al (2008) Attitude estimation for small helicopter using extended kalman filter. In: Proceedings of IEEE Conference on Robotics Automation and Mechatronics, pp 577–581Google Scholar
  24. 24.
    Kallapur AG, Anavatti SG (2006) UAV linear and nonlinear estimation using extended Kalman filter. In: Proceedings of International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and Internet Commerce, p 250Google Scholar
  25. 25.
    Marins JL, Yun X, Bachmann ER et al (2001) An extended Kalman filter for quaternion-based orientation estimation using MARG sensors. In: Proceedings of International Conference on Intelligent Robots and Systems, vol 4, pp 2003–2011Google Scholar
  26. 26.
    Choukroun D, Bar-ltzhack IY, Oshman Y (2006) Novel quaternion Kalman filter. IEEE Trans Aeros Electron Syst 42(1):174–190CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Francesco Caputo
    • 1
  • Alessandro Greco
    • 1
  • Egidio D’Amato
    • 1
  • Immacolata Notaro
    • 1
  • Marco Lo Sardo
    • 2
  • Stefania Spada
    • 3
  • Lidia Ghibaudo
    • 3
  1. 1.Department of EngineeringUniversity of Campania Luigi VanvitelliAversaItaly
  2. 2.LinUp Srl, via ex Aeroporto c/o Consorzio il SolePomigliano D’ArcoItaly
  3. 3.FCA Italy – EMEA Manufacturing Planning and Control – ErgonomicsTorinoItaly

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