Skip to main content

Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data

  • Chapter
  • First Online:
Transactions on Petri Nets and Other Models of Concurrency XI

Part of the book series: Lecture Notes in Computer Science ((TOPNOC,volume 9930))

Abstract

Currently, there is a trend to promote personalized health care in order to prevent diseases or to have a healthier life. Using current devices such as smart-phones and smart-watches, an individual can easily record detailed data from her daily life. Yet, this data has been mainly used for self-tracking in order to enable personalized health care. In this paper, we provide ideas on how process mining can be used as a fine-grained evolution of traditional self-tracking. We have applied the ideas of the paper on recorded data from a set of individuals, and present conclusions and challenges.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://sensor.informatik.uni-mannheim.de.

  2. 2.

    So far, we do not consider the sub-activities in the presented use cases.

  3. 3.

    http://sensor.informatik.uni-mannheim.de/#results.

  4. 4.

    Sequence mining techniques may in principle extract similar patterns. One difference is the inability for these techniques to present process view of the extracted patterns.

References

  1. Adriansyah, A., Sidorova, N., van Dongen, B.F:. Cost-based fitness in conformance checking. In: Application of Concurrency to System Design Conference (ACSD 2011), Kanazawa, Japan, June 2011

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)

    Google Scholar 

  3. Aztiria, A., Izaguirre, A., Basagoiti, R., Augusto, J.C., Cook, D.J.: Automatic modeling of frequent user behaviours in intelligent environments. In: Sixth International Conference on Intelligent Environments (IE), pp. 7–12. IEEE (2010)

    Google Scholar 

  4. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Barkhuus, L., Polichar, V.E.: Empowerment through seamfulness: smartphones in everyday life. Pers. Ubiquit. Comput. 15(6), 629–639 (2011)

    Article  Google Scholar 

  6. Blair, S.N., Church, T.S.: The fitness, obesity, and health equation: is physical activity the common denominator? JAMA 292(10), 1232–1234 (2004)

    Article  Google Scholar 

  7. Bose, R.J.C., van der Aalst, W.M.P.: Process diagnostics using trace alignment: opportunities, issues, and challenges. Inf. Syst. 37(2), 117–141 (2012)

    Article  Google Scholar 

  8. Brown, N.: American heart association. http://www.heart.org. Accessed 29 Apr 2015

  9. Joos, C.A.M.B., van Dongen, B.F., van der Aalst, W.M.P.: Quality dimensions in process discovery: the importance of fitness, precision, generalization and simplicity. Int. J. Coop. Inf. Syst. 23(1), 1440001 (2014)

    Article  Google Scholar 

  10. Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2013)

    Article  Google Scholar 

  11. de Leoni, M., Maggi, F.M., van der Aalst, W.M.P.: An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data. Inf. Syst. 47, 258–277 (2015)

    Article  Google Scholar 

  12. de Leoni, M., van der Aalst, W.M.P.: Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 113–129. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 482–496. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  14. Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Jensen, K., Kristensen, L.M., Wells, L.: Coloured Petrinets, CPN tools for modelling, validation of concurrent systems. STTT 9(3–4), 213–254 (2007)

    Article  Google Scholar 

  16. Jia, Y.: Diatetic and exercise therapy against diabetes mellitus. In: Second International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2009, pp. 693–696. IEEE (2009)

    Google Scholar 

  17. Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9(1), 48–53 (2010)

    Article  Google Scholar 

  18. Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013)

    Article  Google Scholar 

  19. Lara, Ó.D., Pérez, A.J., Labrador, M.A., Posada, J.D.: Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mob. Comput. 8(5), 717–729 (2012)

    Article  Google Scholar 

  20. Leotta, F.: Instrumenting and mining smart spaces. Ph.D. thesis, Universita di Roma - La Sapienza, March 2014

    Google Scholar 

  21. Li, Z.: Spatiotemporal pattern mining: algorithms and applications. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 283–306. Springer, Cham (2014)

    Google Scholar 

  22. Liao, L.: Location-based activity recognition. Ph.D. thesis, University of Washington (2006)

    Google Scholar 

  23. Mans, R.S., van der Aalst, W.M., Vanwersch, R.J.: Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes. Springer, Heidelberg (2015)

    Book  Google Scholar 

  24. Giannotti, F., Nanni, M.: Efficient mining of temporally annotated sequences. In: Proceedings of the Sixth SIAM International Conference on Data Mining, vol. 124, pp. 348. SIAM (2006)

    Google Scholar 

  25. Ordónez, F.J., de Toledo, P., Sanchis, A.: Activity recognition using hybrid generative, discriminative models on home environments using binary sensors. Sensors 13(5), 5460–5477 (2013)

    Article  Google Scholar 

  26. Pesic, M.: Systems, constraint-based workflow management : shifting control to users. Ph.D. thesis, Technische Universiteit Eindhoven (2008)

    Google Scholar 

  27. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Helaoui, R.: Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 149–154. IEEE (2015)

    Google Scholar 

  28. Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)

    Article  Google Scholar 

  29. Serral, E., De Smedt, J., Snoeck, M., Vanthienen, J.: Context-adaptive Petri Nets: Supporting adaptation for the execution context. Expert Syst. Appl. 42(23), 9307–9317 (2015)

    Article  Google Scholar 

  30. Sun, H., De Florio, V., Gui, N., Blondia, C.: Promises, challenges of ambient assisted living systems. In: Sixth International Conference on Information Technology: New Generations, ITNG 2009, pp. 1201–1207. IEEE (2009)

    Google Scholar 

  31. Sztyler, T., Stuckenschmidt, H. On-body localization of wearable devices: an investigation of position-aware activity recognition. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–9. IEEE (2016)

    Google Scholar 

  32. Sztyler, T., Völker, J., Carmona, J., Meier, O., Stuckenschmidt, H.: Discovery of personal processes from labeled sensor data-an application of process mining to personalized health care. In: Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data, ATAED, pp. 31–46 (2015)

    Google Scholar 

  33. van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    MATH  Google Scholar 

  34. van der Aalst, W.M.P, de Beer, H.T., van Dongen, B.F.: Process mining, verification of properties: an approach based on temporal logic. In: CoopIS, Cyprus, pp. 130–147 (2005)

    Google Scholar 

  35. van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  36. Weijters, A.J.M.M., van der Aalst, W.M.P., de Medeiros, A.A.K.: Process mining with the heuristics miner-algorithm. Technical report WP 166, BETA Working Paper Series, Eindhoven University of Technology (2006)

    Google Scholar 

  37. Yin, J., Yang, Q., Pan, J.J.: Sensor-based abnormal human-activity detection. IEEE Trans. Knowl. Data Eng. 20(8), 1082–1090 (2008)

    Article  Google Scholar 

  38. Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)

    Google Scholar 

  39. Zhu, X., Zhu, G., vanden Broucke, S.K.L.M., Vanthienen, J., Baesens, B.: Towards location-aware process modeling and execution. In: Proceedings of the Workshop on Data and Artifact-Centric BPM (DAB 2014), Haifa (Israel), 7–1 September 2014

    Google Scholar 

Download references

Acknowledgments

This work as been partially supported by funds from the Ministry for Economy and Competitiveness (MINECO) of Spain and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timo Sztyler .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sztyler, T., Carmona, J., Völker, J., Stuckenschmidt, H. (2016). Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-53401-4_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53400-7

  • Online ISBN: 978-3-662-53401-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics