Arie Horst, Nirvana Meratnia


In this paper, we explore the capability of wireless sensor networks to perform online activity matching for sport coaching applications. The goal is to design an algorithm to match movements of a trainee and a trainer online and to find their spatial and temporal differences. Such an algorithm can aid the trainer to better observe performance of the trainees in group lessons. We consider fitness-like movements such as those performed in aerobic. We also limit ourselves to only having one sensor node on the trainer and one sensor node on the trainee, however our algorithm scales well to more trainees per trainer. We use Sun SPOT sensor nodes and use the accelerometer and gyroscope sensors to capture the movements. The gravity vector is extracted and improved with a Kalman filter using the accelerometer and gyroscope data. An automatic segmentation technique is developed that examines the movement data for rest and activity periods and changes in movement direction. The segmentation and the movement information are communicated with the node of the trainee where the movements are compared. We choose to use Dynamic TimeWarping (DTW) to perform the spatial and temporal matching of movements. Because DTW is computationally intensive, we develop an optimized technique and provide feedback to the trainee. We test all the design choices extensively using experiments and perform a system test using different test methods to validate our approach.


  1. Aylward, R. (2006). Sensemble: A wireless, compact, multi-user sensor system for interactive dance. In Proc. of NIME 06, pages 134-139.
  2. Bajcsy, R., Borri, A., Benedetto, M. D. D., Giani, A., and Tomlin, C. (2009). Classification of physical interactions between two subjects. Wearable and Implantable Body Sensor Networks, International Workshop on, 0:187-192.
  3. Bellman, R. E. (2003). Dynamic Programming. Dover Publications, Incorporated.
  4. Chambers, G. S., Venkatesh, S., West, G. A. W., and Bui, H. H. (2004). Segmentation of intentional human gestures for sports video annotation. In MMM 7804: Proceedings of the 10th International Multimedia Modelling Conference, page 124, Washington, DC, USA. IEEE Computer Society.
  5. Gafurov, D. and Snekkenes, E. (2009). Gait recognition using wearable motion recording sensors. EURASIP J. Adv. Signal Process, 2009:1-16.
  6. Guenterberg, E., Bajcsy, R., Ghasemezadeh, H., and Jafari, R. (2007). A segmentation technique based on standard deviation in body sensor networks. In DallasEMBS 7807: Proceedings of the IEEE Dallas Engineering in Medicine and Biology Workshop. IEEE.
  7. Guenterberg, E., Ostadabbas, S., Ghasemzadeh, H., and Jafari, R. (2009). An automatic segmentation technique in body sensor networks based on signal energy. In BodyNets 7809: Proceedings of the Fourth International Conference on Body Area Networks, pages 1-7, ICST, Brussels, Belgium, Belgium. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
  8. Haltsonen, S. (1985). Improved dynamic time warping methods for discrete utterance recognition. Acoustics, Speech and Signal Processing, IEEE Transactions on, 33(2):449 - 450.
  9. Jafari, R., Li, W., Bajcsy, R., Glaser, S., and Sastry, S. (2007). Physical activity monitoring for assisted living at home. In BSN 7807: Proceedings of the 4th International Workshop on Wearable and Implantable Body Sensor Networks, pages 213-219, Berlin, Heidelberg. Springer.
  10. Jones, V., in 't Veld, R. H., Tonis, T., Bults, R., van, B. B., Widya, I., Vollenbroek-Hutten, M., and Hermens, H. (2008). Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare. In Proceedings IEEE 10th Workshop on Multimedia Signal Processing, 2008, pages 820-825, Cairns, Australia. IEEE Signal Processing Society.
  11. Kraft, E. (2003). A quaternion-based unscented kalman filter for orientation tracking. In Proceedings of the Sixth International Conference on Information Fusion, volume 1, pages 47-54.
  12. Lester, J., Hannaford, B., and Borriello, G. (2004). Are you with me? using accelerometers to determine if two devices are carried by the same person. In In Proceedings of Second International Conference on Pervasive Computing (Pervasive 2004, pages 33-50.
  13. Lo, B. P. L., Thiemjarus, S., King, R., and zhong Yang, G. (2005). Body sensor network - a wireless sensor platform for pervasive healthcare monitoring. In Adjunct Proceedings of the 3rd International conference on Pervasive Computing (PERVASIVE'05, pages 77- 80.
  14. Marin-Perianu, R., Marin-Perianu, M., Havinga, P., and Scholten, H. (2007). Movement-based group awareness with wireless sensor networks. In PERVASIVE'07: Proceedings of the 5th international conference on Pervasive computing, pages 298-315, Berlin, Heidelberg. Springer-Verlag.
  15. OHGI, Y. (2006). Mems sensor application for the motion analysis in sports science. ABCM Symposium Series in Mechatronics, 2:501-508.
  16. Patel, S., Mancinelli, C., Healey, J., Moy, M., and Bonato, P. (2009). Using wearable sensors to monitor physical activities of patients with copd: A comparison of classifier performance. Wearable and Implantable Body Sensor Networks, International Workshop on, 0:234- 239.
  17. Patterson, D. J., Fox, D., Kautz, H., and Philipose, M. (2005). Fine-grained activity recognition by aggregating abstract object usage. In ISWC 7805: Proceedings of the Ninth IEEE International Symposium on Wearable Computers, pages 44-51, Washington, DC, USA. IEEE Computer Society.
  18. Proakis, J. G. and Manolakis, D. G. (2006). Digital Signal Processing Principles Algorithms and Applications. Prentice hall (4th ed).
  19. Saito, H., Watanabe, T., and Arifin, A. (2009). Ankle and knee joint angle measurements during gait with wearable sensor system for rehabilitation. In World Congress on Medical Physics and Biomedical Engineering, pages 506-509, Berlin, Heidelberg. Springer.
  20. Tran, D. and Sorokin, A. (2008). Human activity recognition with metric learning. In ECCV 7808: Proceedings of the 10th European Conference on Computer Vision, pages 548-561, Berlin, Heidelberg. Springer-Verlag.
  21. van Kasteren, T. and Krose, B. (2007). Bayesian activity recognition in residence for elders. In Intelligent Environments, 2007. IE 07. 3rd IET International Conference on, pages 209-212.
  22. Watkinson, J. (1993). The Art of Digital Audio. Butterworth-Heinemann, Newton, MA, USA, 2nd edition.
  23. Whitehead, A. and Fox, K. (2009). Device agnostic 3d gesture recognition using hidden markov models. In Future Play 7809: Proceedings of the 2009 Conference on Future Play on @ GDC Canada, pages 29-30, New York, NY, USA. ACM.
  24. Wirz, M., Roggen, D., and Troster, G. (2009). Decentralized detection of group formations from wearable acceleration sensors. Computational Science and Engineering, IEEE International Conference on, 4:952- 959.
  25. Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., and Rehg, J. M. (2007). A scalable approach to activity recognition based on object use. In In Proceedings of the International Conference on Computer Vision (ICCV), Rio de.
  26. Yin, X. and Xie, M. (2007). Finger identification and hand posture recognition for human-robot interaction. Image and Vision Computing, 25(8):1291 - 1300.
  27. Yun, X., Lizarraga, M., Bachmann, E., and McGhee, R. (2003). An improved quaternion-based kalman filter for real-time tracking of rigid body orientation. In Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, volume 2, pages 1074-1079.

Paper Citation

in Harvard Style

Horst A. and Meratnia N. (2011). ONLINE ACTIVITY MATCHING USING WIRELESS SENSOR NODES . In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8425-48-5, pages 22-31. DOI: 10.5220/0003361100220031

in Bibtex Style

author={Arie Horst and Nirvana Meratnia},
booktitle={Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
SN - 978-989-8425-48-5
AU - Horst A.
AU - Meratnia N.
PY - 2011
SP - 22
EP - 31
DO - 10.5220/0003361100220031