Detecting and Interpreting Muscle Activity with Wearable Force Sensors

  • Paul Lukowicz
  • Friedrich Hanser
  • Christoph Szubski
  • Wolfgang Schobersberger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3968)


In this paper we present a system for assessing muscle activity by using wearable force sensors placed on the muscle surface. Such sensors are very thin, power efficient and have also been demonstrated as pure textile devices, so that they can be easily integrated in such garments as elastic underwear or tight shorts/shirt. On the example upper-leg muscle we show how good signal quality can be reliably acquired under realistic conditions. We then show how information about general user context can be derived from the muscle activity signal. We first look at the modes of locomotion problem which is a well studied, benchmark-like problem in the community. We then demonstrate the correlation between the signals from our system and user fatigue. We conclude with a discussion of other types of information that can be derived from the muscle activity based on physiological considerations and example data form our experiments.


Muscle Activity Activity Recognition Muscle Fatigue Force Sensor Stance Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lukowicz, P., Ward, J.A., Junker, H., Stäger, M., Tröster, G., Atrash, A., Starner, T.: Recognizing workshop activity using body worn microphones and accelerometers. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 18–32. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Lukowicz, P., Kirstein, T., Tröster, G.: Wearable systems for health care applications. Methods of Information in Medicine 43, 232–238 (2004)Google Scholar
  3. 3.
    Moritani, T., Yoshitake, Y.: The use of electromyography in applied physiology. Journal of Electromyography and Kinesiology 8, 363–381 (1998)CrossRefGoogle Scholar
  4. 4.
    Merletti, R.: Standards for reporting EMG data. Journal of Electromyography and Kinesiology 9, 3–4 (1999)Google Scholar
  5. 5.
    Orizio, C., Gobbo, M., Diemont, B., Esposito, F., Veicsteinas, A.: The surface mechanomyogram as a tool to describe the influence of fatigue on biceps brachii motor unit activation strategy. Historical basis and novel evidence. European Journal of Applied Physiology 90, 326–336 (2003)CrossRefGoogle Scholar
  6. 6.
    Paradiso, J.A., Hsiao, K., Benbasat, A.Y., Teegarden, Z.: Design and implementation of expressive footwear. IBM Systems Journal 39, 511–529 (2000)CrossRefGoogle Scholar
  7. 7.
    Junker, H., Lukowicz, P., Tröster, G.: Locomotion analysis using a simple feature derived from force sensitive resistors. In: Proc. 2nd International Conference on Biomedical Engineering (2004)Google Scholar
  8. 8.
    Antifakos, S., Michahelles, F., Schiele, B.: Proactive instructions for furniture assembly. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 351–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Kern, N., Schiele, B., Schmidt, A.: Multi-sensor activity context detection for wearable computing. In: Proc. European Symposium on Ambient Intelligence, pp. 220–232 (2003)Google Scholar
  10. 10.
    Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: 2001 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 747–752 (2001)Google Scholar
  11. 11.
    Randell, C., Muller, H.: Context awareness by analysing accelerometer data. In: ISWC, pp. 175–176 (2000)Google Scholar
  12. 12.
    Seon-Woo, L., Mase, K.: Recognition of walking behaviors for pedestrian navigation. In: Proc. IEEE International Conference on Control Applications, pp. 1152–1155 (2001)Google Scholar
  13. 13.
    Van-Laerhoven, K., Cakmakci, O.: What shall we teach our pants? In: Proc. 4th International Symposium on Wearable Computers, pp. 77–83 (2000)Google Scholar
  14. 14.
    Luinge, H.J., Veltink, P.H., Baten, C.T.M.: Estimation of orientation with gyroscopes and accelerometers. In: Proc. First Joint BMES/EMBS Conference, vol. 2, p. 844 (1999)Google Scholar
  15. 15.
    Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C.J., Robert, P.: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Transactions on Biomedical Engineering 50, 711–723 (2003)CrossRefGoogle Scholar
  16. 16.
    Sekine, M., Tamura, T., Fujimoto, T., Fukui, Y.: Classification of walking pattern using acceleration waveform in elderly people. Engineering in Medicine and Biology Society 2, 1356–1359 (2000)Google Scholar
  17. 17.
    Tamura, T., Abe, Y., Sekine, M., Fujimoto, T., Higashi, Y., Sekimoto, M.: Evaluation of gait parameters by the knee accelerations. Engineering in Medicine and Biology 2, 828 (1999)Google Scholar
  18. 18.
    van den Bogert, A.J., Read, L., Nigg, B.M.: A method for inverse dynamic analysis using accelerometry. Journal of Biomechanics 29, 949–954 (1996)CrossRefGoogle Scholar
  19. 19.
    Junker, H., Lukowicz, P., Tröster, G.: Padnet: Wearable physical activity detection network. In: Proceedings of the 7th International Symposium on Wearable Computers, pp. 244–245 (2003)Google Scholar
  20. 20.
    James, B., Parker, A.W.: Electromyography of stair locomotion in elderly men and women. Electromyography and Clinical Neurophysiology 29, 161–168 (1989)Google Scholar
  21. 21.
    Kale, A., Rajagopalan, A.N., Cuntoor, N., Kruger, V.: Gait-based recognition of humans using continuous HMMs. In: Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 336–341. IEEE Computer Society, Washington, DC, USA (2002)CrossRefGoogle Scholar
  22. 22.
    Heino-Brechter, J., Powers, C.M.: Patellofemoral joint stress during stair ascent and descent in persons with and without patellofemoral pain. Gait Posture 16, 115–123 (2002)CrossRefGoogle Scholar
  23. 23.
    Kuster, M., Sakurai, S., Wood, G.A.: Kinematic and kinetic comparison of downhill and level walking. Clinical Biomechanics 10, 79–84 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul Lukowicz
    • 1
  • Friedrich Hanser
    • 1
  • Christoph Szubski
    • 1
    • 2
  • Wolfgang Schobersberger
    • 2
  1. 1.Institute for Computer Systems and Networks, UMITHallAustria
  2. 2.Research Department for Leisure, Travel and Alpine Medicine, UMITHallAustria

Personalised recommendations