Advertisement

Focus on the User: A User Relative Coordinate System for Activity Detection

  • Andreas JahnEmail author
  • Marek Bachmann
  • Philipp Wenzel
  • Klaus David
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10257)

Abstract

The information about a person’s activity, as a special type of contextual information, can be used for a multitude of applications. Thus, activity detection is a focus in research. For activity detection, usually measured sensor data are processed and matched to known activities. Unfortunately, measured sensor data can be inconsistent for the same activity. Depending on the device orientation and in which direction of compass the user is heading (while carrying out an activity) the sensor data changes. As a result, expected values on one sensor axis appear on another one and it is challenging for a pre-generated activity detection model to match the sensor data to the activity. However, the intuitive solution seems to be easy: focus on the user. Regardless of the device orientation or the cardinal direction, the activity never change for the user. In this paper, we present an approach to focus on the user in activity detection. We convert sensor data into a representation relative to the user. Thus, the sensor data stay consistent regardless of the device orientation or the cardinal direction. We show that by using the focus on the user approach the detection rates increase up to 21.7%. Also, the detection is more reliable (lower standard deviation). We prove the reliable activity detection for different device orientations, cardinal directions, types of algorithms, sensor sets, activities, and sensor positions.

Keywords

Context awareness Activity detection User oriented Device orientation independence Coordinate system 

Notes

Acknowledgments

This work has been [co-]funded by the Social Link Project within the Loewe Program of Excellence in Research, Hesse, Germany.

References

  1. 1.
    Weka 3 - Data Mining with Open Source Machine Learning Software in Java (2016). http://www.cs.waikato.ac.nz/ml/weka/index.html
  2. 2.
    Ali, A.S., Georgy, J., Bruce Wright, D.: Estimation of heading misalignment between a pedestrian and a wearable device. In: International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6, June 2014Google Scholar
  3. 3.
    Borazio, M., Van Laerhoven, K.: Using time use with mobile sensor data: a road to practical mobile activity recognition? In: International Conference on Mobile and Ubiquitous Multimedia, pp. 1–10. ACM, Luleå (2013)Google Scholar
  4. 4.
    Dernbach, S., Das, B., Krishnan, N.C., Thomas, B., Cook, D.: Simple and complex activity recognition through smart phones. In: Intelligent Environments (IE), pp. 214–221, June 2012Google Scholar
  5. 5.
    Henpraserttae, A., Thiemjarus, S., Marukatat, S.: Accurate activity recognition using a mobile phone regardless of device orientation and location. In: International Conference on Body Sensor Networks (BSN), pp. 41–46, May 2011Google Scholar
  6. 6.
    Ichikawa, F., Chipchase, J., Grignani, R.: Where’s the phone? A study of mobile phone location in public spaces. In: Mobile Technology, Applications and Systems, pp. 1–8. IEEE (2005)Google Scholar
  7. 7.
    Kunze, K., Lukowicz, P., Partridge, K., Begole, B.: Which way am I facing: inferring horizontal device orientation from an accelerometer signal. In: International Symposium on Wearable Computers (ISWC), pp. 149–150. IEEE, Linz, September 2009Google Scholar
  8. 8.
    Kusber, R., David, K., Klein, B.N.: A novel future internet smart grid application for energy management in offices. In: Future Network and Mobile Summit (FutureNetworkSummit), pp. 1–10, July 2013Google Scholar
  9. 9.
    Kusber, R., Memon, A.Q., Kroll, D., David, K.: Direction detection of users independent of smartphone orientations. In: Vehicular Technology Conference (VTC Fall), pp. 1–6. IEEE, September 2015Google Scholar
  10. 10.
    Lau, S.L.: Comparison of orientation-independent-based-independent-based movement recognition system using classification algorithms. In: IEEE Symposium on Wireless Technology and Applications (ISWTA), pp. 322–326, September 2013Google Scholar
  11. 11.
    Lau, S.L., Konig, I., David, K., Parandian, B., Carius-Dussel, C., Schultz, M.: Supporting patient monitoring using activity recognition with a smartphone. In: International Symposium on Wireless Communication Systems (ISWCS), pp. 810–814 (2010)Google Scholar
  12. 12.
    Mizell, D.: Using gravity to estimate accelerometer orientation. In: International Symposium on Wearable Computers (ISWC), p. 252. IEEE (2003)Google Scholar
  13. 13.
    Scholl, P., van Laerhoven, K.: A feasibility study of wrist-worn accelerometer based detection of smoking habits. In: Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 886–891, July 2012Google Scholar
  14. 14.
    Shoaib, M., Bosch, S., Incel, O., Scholten, H., Havinga, P.: A survey of online activity recognition using mobile phones. Sensors 15(1), 2059–2085 (2015)CrossRefGoogle Scholar
  15. 15.
    Shoaib, M., Scholten, H., Havinga, P.: Towards physical activity recognition using smartphone sensors. In: IEEE Conference on Ubiquitous Intelligence and Computing and Conference on Autonomic and Trusted Computing (UIC/ATC), pp. 80–87, December 2013Google Scholar
  16. 16.
    Thiemjarus, S.: A device-orientation independent method for activity recognition. In: Body Sensor Networks (BSN), pp. 19–23. IEEE, June 2010Google Scholar
  17. 17.
    Ustev, Y.E., Durmaz Incel, O., Ersoy, C.: User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal. In: Pervasive and Ubiquitous Computing Adjunct Publication, pp. 1427–1436. ACM (2013)Google Scholar
  18. 18.
    Yang, J.: Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of 1st International Workshop on Interactive Multimedia for Consumer Electronics. ACM, Beijing (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andreas Jahn
    • 1
    Email author
  • Marek Bachmann
    • 1
  • Philipp Wenzel
    • 1
  • Klaus David
    • 1
  1. 1.Chair for Communication Technology (ComTec), Faculty of Electrical Engineering and Computer ScienceUniversity of KasselKasselGermany

Personalised recommendations