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Activity Recognition Using Biomechanical Model Based Pose Estimation

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Smart Sensing and Context (EuroSSC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 6446))

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Abstract

In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.

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References

  1. Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Proc. 2nd Int. Conf. Pervasive Comput., pp. 1–17 (2004)

    Google Scholar 

  2. Bieber, G., Peter, C.: Using physical activity for user behavior analysis. In: PETRA 2008 (2008)

    Google Scholar 

  3. Ermes, M., Pärkkä, J., Cluitmans, L.: Advancing from offline to online activity recognition with wearable sensors. In: 30th Annual International IEEE EMBS Conference, pp. 4451–4454 (2008)

    Google Scholar 

  4. Ermes, M., Pärkkä, J., Mäntyjärvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 12(1), 20–26 (2008)

    Article  Google Scholar 

  5. Ferraris, F., Grimaldi, U., Parvis, M.: Procedure for Effortless In-Field Calibration of Three-Axis Rate Gyros and Accelerometers. Sensor and Materials 7, 311–330 (1995)

    Google Scholar 

  6. Harada, T., Mori, T., Sato, T.: Development of a Tiny Orientation Estimation Device to Operate under Motion and Magnetic Disturbance. The International Journal of Robotics Research 26, 547–559 (2007)

    Article  Google Scholar 

  7. Hu, X., Liu, Y., Wang, Y., Hu, Y., Yan, D.: Autocalibration of an Electronic Compass for Augmented Reality. In: ISMAR (2005)

    Google Scholar 

  8. Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: sOc-EUSAI 2005, pp. 159–163 (2005)

    Google Scholar 

  9. InterSense, http://www.intersense.com

  10. Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Mathematics in Science and Engineering, vol. 64. Academic Press, Inc., London (1970)

    Book  MATH  Google Scholar 

  11. Karantonis, D., Narayanan, M., Mathie, M., Lovell, N., Celler, B.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006)

    Article  Google Scholar 

  12. Long, X., Yin, B., Aarts, R.M.: Single-accelerometer based daily physical activity classification. In: 31st Annual International IEEE EMBS Conference, pp. 6107–6110 (2009)

    Google Scholar 

  13. Luinge, H.J., Veltink, P.H., Baten, C.T.M.: Ambulatory measurement of arm orientation. Journal of Biomechanics (40), 78–85 (2007)

    Google Scholar 

  14. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  15. Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 10(1), 119–128 (2006)

    Article  Google Scholar 

  16. Pirttikangas, S., Fujinami, K., Nakajima, T.: Feature selection and activity recognition from wearable sensors. In: Youn, H.Y., Kim, M., Morikawa, H. (eds.) UCS 2006. LNCS, vol. 4239, pp. 516–527. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Schepers, H.M., Roetenberg, D., Veltink, P.H.: Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation. Medical and Biological Engineering and Computing (48), 27–37 (2010)

    Google Scholar 

  18. Trivisio, http://www.trivisio.com

  19. Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W., Popovic, J.: Practical Motion Capture in Everyday Surroundings. In: SIGGRAPH (2007)

    Google Scholar 

  20. XSens, http://www.xsens.com

  21. Zhou, H., Hu, H.: Inertial sensors for motion detection of human upper limbs. Sensor Review 27(8), 151–158 (2007)

    Article  Google Scholar 

  22. Zinnen, A., Blanke, U., Schiele, B.: An Analysis of Sensor-Oriented vs. Model-Based Activity Recognition. ISWC, pp. 93–100 (2009)

    Google Scholar 

  23. Zinnen, A., Wojek, C., Schiele, B.: Multi activity recognition based on bodymodel-derived primitives. LoCA, pp. 1–18 (2009)

    Google Scholar 

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Reiss, A., Hendeby, G., Bleser, G., Stricker, D. (2010). Activity Recognition Using Biomechanical Model Based Pose Estimation. In: Lukowicz, P., Kunze, K., Kortuem, G. (eds) Smart Sensing and Context. EuroSSC 2010. Lecture Notes in Computer Science, vol 6446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16982-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-16982-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16981-6

  • Online ISBN: 978-3-642-16982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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