Abstract
The analysis of business processes using process mining requires structured log data. Regarding manual activities, this data can be generated from sensor data acquired from the Internet of Things. The main objective of this paper is the development and evaluation of an approach which recognizes and logs manually performed activities, enabling the application of established process discovery methods. A system was implemented which uses a body area network, image data of the process environment and feedback from the executing workers in case of uncertainties during detection. Both feedback and image data are acquired and processed during process execution. In a case study in a laboratory environment, the system was evaluated using an example process. The implemented approach shows that the inclusion of image data of the environment and user feedback in ambiguous situations during recognition generate log data which well represent actual process behavior.
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
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2, 182–192 (2012)
Cameranesi, M., Diamantini, C., Potena, D.: Discovering process models of activities of daily living from sensors. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 285–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_21
Dimaggio, M., Leotta, F., Mecella, M., Sora, D.: Process-based habit mining: experiments and techniques. In: International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp. 145–152. IEEE (2016)
Farkas, I., Doran, E.: Activity recognition from acceleration data collected with a tri-axial accelerometer. Acta Technica Napocensis - Electron. Telecommun. 52, 38–43 (2011)
Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. Des. Sci. IS Res. MIS Q. 28(1), 75–105 (2004)
Janiesch, C., et al.: The Internet-of-Things meets business process management: mutual benefits and challenges (2017)
Knoch, S., Ponpathirkoottam, S., Fettke, P., Loos, P.: Technology-enhanced process elicitation of worker activities in manufacturing. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 273–284. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_20
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)
Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Leotta, F., Mecella, M., Mendling, J.: Applying process mining to smart spaces: perspectives and research challenges. In: Persson, A., Stirna, J. (eds.) CAiSE 2015. LNBIP, vol. 215, pp. 298–304. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19243-7_28
Mannhardt, F., Bovo, R., Oliveira, M.F., Julier, S.: A taxonomy for combining activity recognition and process discovery in industrial environments. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11315, pp. 84–93. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03496-2_10
Raso, R., Emrich, A., Burghardt, T., Schlenker, M., Sträter, O., Fettke, P., Loos, P.: Activity monitoring using wearable sensors in manual production processes - an application of CPS for automated ergonomic assessments. In: Drews, P., Funk, B., Niemeyer, P., Xie, L. (eds.) Multikonferenz Wirtschaftsinformatik 2018, Lüneburg , pp. 231–242 (2018)
Sora, D., Leotta, F., Mecella, M.: An habit is a process: a BPM-based approach for smart spaces. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 298–309. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_22
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)
van der Aalst, W.M.P.: Process Mining-Data Science in Action, vol. 2, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distrib. Parallel Databases 14(1), 5–51 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rebmann, A., Emrich, A., Fettke, P. (2019). Enabling the Discovery of Manual Processes Using a Multi-modal Activity Recognition Approach. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-37453-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37452-5
Online ISBN: 978-3-030-37453-2
eBook Packages: Computer ScienceComputer Science (R0)