Abstract
Skilled workers are over-proportionally exposed to physical stress and hazards, which often means that their work is characterized by high physical demands. In this paper we deal with a proof of concept, which uses wearable sensors to monitor the movement of workers to automatically identify working gestures and poses which result in high physical stresses. Based on a rating system a real-time alerting on unhealthy positions is initiated. Apart from solutions concerning individual persons, time series data from all the workers at the site can be used to create a smart schedule optimizing the process flow and minimizing individual physical stresses. Altogether, we use the interrelations between everyday behavior and health problems to approach one of the greatest common goals of both employers and employees – the goal of “staying healthy”.
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
Notes
- 1.
IMU is a device consisting sensors like accelerometer, gyroscope, magnetometer etc.
- 2.
Kinetics refers to the study of motion and angles while kinematics also takes the underlying forces into account.
- 3.
Non-deterministic Polynomial-time hardness: Probably not solvable in polynomial time.
- 4.
Hierarchical Task Network.
References
BAuA (Bundesanstalt für Arbeitsschutz und Arbeitsmedizin). Sicherheit und Gesundheit bei der Arbeit – Berichtsjahr 2016 - Unfallverhütungsbericht Arbeit Sicherheit und Gesundheit (2017)
Karhu, O., Kansi, P., Kuorinka, I.: Correcting working postures in industry: a practical method for analysis. Appl. Ergon. 8, 199–201 (1977)
Kivi, P., Mattila, M.: Analysis and improvement of work postures in the building industry: application of the computerised OWAS method. Appl. Ergon. 22, 43–48 (1991)
Ellegast, R.: Messung von Muskel-Skelett-Belastungen mit dem CUELA-Messsystem. Aus der Arbeit des IFA 50–51 (2013)
Lietz, R.: CUELA-Feedback: Körperhaltungs-Check mit dem Smartphone (2016)
Gräbener, T., Horn, U.: PREFLOW Abschlussbericht Uni Kassel (2006)
DGUV Fliesenleger mit CUELA-Messsystem. https://www.dguv.de/ifa/fachinfos/ergonomie/kniebelastende-taetigkeiten/index.jsp. Accessed 7 Feb 2019
Alwasel, A., Elrayes, K., Abdel-Rahman, E.M., Haas, C.: Sensing Construction Work-Related Musculoskeletal Disorders (WMSDs), pp. 164–169 (2011). https://doi.org/10.22260/ISARC2011/0027
Brandl, C., Bonin, D., Mertens, A., et al.: Digitalisierungsansätze ergonomischer Analysen und Interventionen am Beispiel der markerlosen Erfassung von Körperhaltungen bei Arbeitstätigkeiten in der Produktion. Z Arbeitswiss 70, 89–98 (2016). https://doi.org/10.1007/s41449-016-0016-9
Bau, B.G.: Betriebsärztlicher Gesundheitsbericht für Maler
Bau, B.G.: Ergonomie am Bau Damit es leichter geht Damit es leichter geht (2013)
Koningsveld, E.A.P., der Molen, H.F.: History and future of ergonomics in building and construction. Ergonomics 40, 1025–1034 (1997)
Schneider, S., Susi, P.: Ergonomics and construction: a review of potential hazards in new construction. Am. Ind. Hyg. Assoc. J. 55, 635–649 (1994)
Jaffar, N., Abdul-Tharim, A.H., Mohd-Kamar, I.F., Lop, N.S.: A literature review of ergonomics risk factors in construction industry. Procedia Eng. 20, 89–97 (2011). https://doi.org/10.1016/j.proeng.2011.11.142
DGUV. Arbeitsbedingte Gesundheits gefahren (2015)
Wang, D., Dai, F., Ning, X.: Risk assessment of work-related musculoskeletal disorders in construction: state-of-the-art review. J. Constr. Eng. Manage. 141, 04015008 (2015). https://doi.org/10.1037/a0021167
Killough, M.K., Crumpton, L.L.: An investigation of cumulative trauma disorders in the construction industry. Int. J. Ind. Ergon. 18, 399–405 (1996)
Plus E A Step-by-Step Guide Rapid Upper Limb Assessment (RULA)
Plus E A Step-by-Step Guide Rapid Entire Body Assessment (REBA)
Inc. XNA (2019). XSENS. https://www.xsens.com/. Accessed 16 Apr 2019
Skeleton, M.J.
Kulcsár, G., Erdélyi, F.: A new approach to solve multi-objective scheduling and rescheduling tasks. Int. J. Comput. Intell. Res. 3, 343–351 (2007)
Norvig, P., Russel, S.: Artificial Intelligence - A Modern Approach
Schuster, Mike, Paliwal, Kuldip K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Hannun, A., et al.: Resource-constrained project scheduling problem: review of past and recent developments. J. Proj. Manage. 3, 55–88 (2018). https://doi.org/10.5267/j.jpm.2018.1.005
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: BRITS: Bidirectional Recurrent Imputation for Time Series. In: NeurIPS (2018)
Acknowledgements
This work is based on BauPrevent, a project partly funded by the German ministry of education and research (BMBF) and the European Social Fund (ESF) as part of the “Zukunft der Arbeit: Mittelstand - innovativ und sozial” program, reference number 02L17C011. The authors are responsible for the content of this publication.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Derouet, M., Nagaraj, D., Schake, E., Werth, D. (2019). Towards a Digitized Understanding of the Skilled Crafts Domain. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-36691-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-36691-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36690-2
Online ISBN: 978-3-030-36691-9
eBook Packages: Computer ScienceComputer Science (R0)