Abstract
This work aimed to investigate the contribution of each single joint during the execution of whole-body reaching tasks to the overall discomfort of the worker evaluated through standard observational methods.
Forty-five healthy volunteers were asked to reach and rotate 2 spheres placed on a custom-made rack in standardized positions, i.e., above the head and one at floor level at the center side. Whole-body kinematics was acquired via a system based on wearable inertial measurement units, which represent proper enabling technologies within human-centered “Industry 5.0” context. Standard ergonomic scales including RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), and MMGA (Method for Movement and Gesture Assessment), were assessed for each subject and each sphere position. Moreover, a quantitative index based on actual joint kinematics, i.e., the W1 index, was computed for each joint angle involved in the task. Pearson’s correlation analysis was performed for W1 relative to each joint with respect to RULA, REBA, and MMGA scores.
Considering REBA and MMGA scores, the most comfortable reaching areas were the ones in which the sphere was positioned at the top; in contrast, the lowest positions evidenced the most increased discomfort indexes. The RULA did not result sensitive to the different positions, while REBA and MMGA seemed to be more influenced by the range of motion of the lower limb joint angles than the upper limb ones.
This study underlines the necessity to focus on multiple potential contributors to work-related musculoskeletal disorders and underlines the importance of subject-specific approaches toward risk assessment by exploiting quantitative measurements and wearable technologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eurofound First findings: Sixth European working conditions survey (2015)
Manghisi, V.M., Uva, A.E., Fiorentino, M., Bevilacqua, V., Trotta, G.F., Monno, G.: Real time RULA assessment using Kinect v2 sensor. Appl. Ergon. 65, 481–491 (2017). https://doi.org/10.1016/j.apergo.2017.02.015
Myrelid, A., Olhager, J.: Hybrid manufacturing accounting in mixed process environments: a methodology and a case study. Int. J. Prod. Econ. 210, 137–144 (2019). https://doi.org/10.1016/j.ijpe.2019.01.024
Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and industry 5.0—inception, conception and perception. J. Manuf. Syst. 61, 530–535 (2021). https://doi.org/10.1016/j.jmsy.2021.10.006
Leng, J., et al.: Industry 5.0: prospect and retrospect. J. Manuf. Syst. 65, 279–295 (2022). https://doi.org/10.1016/j.jmsy.2022.09.017
Maddikunta, P.K.R., et al.: Industry 5.0: a survey on enabling technologies and potential applications. J. Ind. Inf. Integr. 26, 100257 (2022). https://doi.org/10.1016/j.jii.2021.100257
Bortolini, M., Gamberi, M., Pilati, F., Regattieri, A.: Automatic assessment of the ergonomic risk for manual manufacturing and assembly activities through optical motion capture technology. Procedia CIRP 72, 81–86 (2018). https://doi.org/10.1016/j.procir.2018.03.198
Li, G., Buckle, P.: Current techniques for assessing physical exposure to work-related musculoskeletal risks, with emphasis on posture-based methods. Ergonomics 42, 674–695 (1999). https://doi.org/10.1080/001401399185388
David, G.C.: Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders. Occup. Med. (Chic. Ill) 55, 190–199 (2005). https://doi.org/10.1093/occmed/kqi082
Murphy, M.A., Willén, C., Sunnerhagen, K.S.: Kinematic variables quantifying upper-extremity performance after stroke during reaching and drinking from a glass. Neurorehabil. Neural Repair 25, 71–80 (2011). https://doi.org/10.1177/1545968310370748
Colombini, D., Occhipinti, E.: Preventing upper limb work-related musculoskeletal disorders (UL-WMSDS): New approaches in job (re)design and current trends in standardization. Appl. Ergon. 37, 441–450 (2006). https://doi.org/10.1016/j.apergo.2006.04.008
Karhu, O., Kansi, P., Kuorinka, I.: Correcting working postures in industry: a practical method for analysis. Appl. Ergon. 8, 199–201 (1977). https://doi.org/10.1016/0003-6870(77)90164-8
McAtamney, L., Nigel Corlett, E.: RULA: a survey method for the investigation of work-related upper limb disorders. Appl. Ergon. 24, 91–99 (1993). https://doi.org/10.1016/0003-6870(93)90080-S
Hignett, S., McAtamney, L.: Rapid entire body assessment (REBA). Appl. Ergon. 31, 201–205 (2000). https://doi.org/10.1016/S0003-6870(99)00039-3
Andreoni, G., Mazzola, M., Ciani, O., Zambetti, M., Romero, M., Costa, F., Preatoni, E.: Method for movement and gesture assessment (MMGA) in ergonomics. In: Duffy, V.G. (ed.) ICDHM 2009. LNCS, vol. 5620, pp. 591–598. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02809-0_62
Kee, D., Karwowski, W.: LUBA: an assessment technique for postural loading on the upper body based on joint motion discomfort and maximum holding time. Appl. Ergon. 32, 357–366 (2001). https://doi.org/10.1016/S0003-6870(01)00006-0
Lorenzini, M., Kim, W., Ajoudani, A.: An online multi-index approach to human ergonomics assessment in the workplace. IEEE Trans. Hum.-Mach. Syst. 52, 812–823 (2022). https://doi.org/10.1109/THMS.2021.3133807
Oldfielf, R.C.: The assesment and analysis of handedness: the Edimburgh inventory. Neuropsychologia 9, 97–113 (1971). https://doi.org/10.1007/978-0-387-79948-3_6053
Michaelsen, S.M., Jacobs, S., Roby-Brami, A., Levin, M.F.: Compensation for distal impairments of grasping in adults with hemiparesis. Exp. Brain Res. 157, 162–173 (2004). https://doi.org/10.1007/s00221-004-1829-x
Whitmore, M., Boyer, J., Holubec, K.: NASA-STD-3001, space flight human-system standard and the human integration design handbook. In: Proceedings of the Industrial and Systems Engineering Research Conference (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Scalona, E., De Marco, D., Avanzini, P., Fabbri Destro, M., Andreoni, G., Lopomo, N.F. (2023). Human Ergonomic Assessment Within “Industry 5.0” Workplace: Do Standard Observational Methods Correlate with Kinematic-Based Index in Reaching Tasks?. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. https://doi.org/10.1007/978-3-031-35741-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-35741-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35740-4
Online ISBN: 978-3-031-35741-1
eBook Packages: Computer ScienceComputer Science (R0)