Measuring the Performance of Push-ups - Qualitative Sport Activity Recognition

Sebastian Baumbach, Andreas Dengel

2017

Abstract

The trend of mobile activity monitoring using widely available technology is one of the most blooming concepts in the recent years. It supports many novel applications, such as fitness games or health monitoring. In these scenarios, activity recognition tries to distinguish between different types of activities. However, only little work has focused on qualitative recognition so far: How exactly is the activity carried out? In this paper, an approach for supervising activities, i.e. qualitative recognition, is proposed. The focus lied on push-ups as a proof of concept, for which sensor data of smartphones and smartwatches were collected. A user-dependent dataset with 4 participants and a user-independent dataset with 16 participants were created. The performance of Naive Bayes classifier was tested against normal, kernel and multivariate multinomial probability distributions. An accuracy of 90.5% was achieved on the user-dependent model, whereas the user-independent model scored with an accuracy of 80.3%.

References

  1. Albinali, F., Intille, S., Haskell, W., and Rosenberger, M. (2010). Using wearable activity type detection to improve physical activity energy expenditure estimation. In Proceedings of the 12th ACM international conference on Ubiquitous computing, pages 311-320. ACM.
  2. Babyak, M. A. (2004). What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic medicine, 66(3):411-421.
  3. Bao, L. and Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In Pervasive computing, pages 1-17. Springer.
  4. Baumann, K. (2003). Cross-validation as the objective function for variable-selection techniques. TrAC Trends in Analytical Chemistry, 22(6):395-406.
  5. Brezmes, T., Gorricho, J.-L., and Cotrina, J. (2009). Activity recognition from accelerometer data on a mobile phone. In Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living, pages 796-799. Springer.
  6. Campbell, A. and Choudhury, T. (2012). From smart to cognitive phones. IEEE Pervasive Computing, (3):7- 11.
  7. Chang, K.-H., Chen, M. Y., and Canny, J. (2007). Tracking free-weight exercises. Springer.
  8. Esterman, M., Tamber-Rosenau, B. J., Chiu, Y.-C., and Yantis, S. (2010). Avoiding non-independence in fmri data analysis: leave one subject out. Neuroimage, 50(2):572-576.
  9. John, G. H. and Langley, P. (1995). Estimating continuous distributions in bayesian classifiers. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, pages 338-345. Morgan Kaufmann Publishers Inc.
  10. Juan, A. and Ney, H. (2002). Reversing and smoothing the multinomial naive bayes text classifier. In PRIS, pages 200-212.
  11. Krishnan, N. C., Colbry, D., Juillard, C., and Panchanathan, S. (2008). Real time human activity recognition using tri-axial accelerometers. In Sensors, signals and information processing workshop.
  12. Kuntze, G., Pias, M., Bezodis, I., Kerwin, D., Coulouris, G., and Irwin, G. (2009). Use of on-body sensors to support elite sprint coaching. International Association of Computer Science in Sport, pages 71-75.
  13. Langley, P., Iba, W., and Thompson, K. (1992). An analysis of bayesian classifiers. In Aaai, volume 90, pages 223-228.
  14. Lee, M. (2009). Physical activity recognition using a single tri-axis accelerometer. In Proceedings of the world congress on engineering and computer science, volume 1.
  15. Lester, J., Choudhury, T., and Borriello, G. (2006). A practical approach to recognizing physical activities. In Pervasive Computing, pages 1-16. Springer.
  16. Long, X., Yin, B., and Aarts, R. M. (2009). Singleaccelerometer-based daily physical activity classification. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 6107-6110. IEEE.
  17. McClaran, S. R. (2001). The effectiveness of personal training on changing attitudes towards physical activity. Medicine & Science in Sports & Exercise, 33(5):S211.
  18. Michahelles, F. and Schiele, B. (2005). Sensing and monitoring professional skiers. Pervasive Computing, IEEE, 4(3):40-45.
  19. Möller, A., Roalter, L., Diewald, S., Scherr, J., Kranz, M., Hammerla, N., Olivier, P., and Pl ötz, T. (2012). Gymskill: A personal trainer for physical exercises. In Pervasive Computing and Communications (PerCom), pages 213-220. IEEE.
  20. Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., and Korhonen, I. (2006). Activity classification using realistic data from wearable sensors. Information Technology in Biomedicine, IEEE Transactions on, 10(1):119-128.
  21. Rasekh, A., Chen, C.-A., and Lu, Y. (2014). Human activity recognition using smartphone. arXiv preprint arXiv:1401.8212.
  22. Ravi, N., Dandekar, N., Mysore, P., and Littman, M. L. (2005). Activity recognition from accelerometer data. In AAAI, volume 5, pages 1541-1546.
  23. Saponas, T., Lester, J., Froehlich, J., Fogarty, J., and Landay, J. (2008). ilearn on the iphone: Real-time human activity classification on commodity mobile phones. University of Washington CSE Tech Report UW-CSE08-04-02, 2008.
  24. Sefen, B., Baumbach, S., Dengel, A., and Abdennadher, S. (2016). Human activity recognition - using sensor data of smartphones and smartwatches. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence , volume 2, pages 488-493. SCITEPRESS.
  25. Sharma, A., Purwar, A., Lee, Y.-D., Lee, Y.-S., and Chung, W.-Y. (2008). Frequency based classification of activities using accelerometer data. In Multisensor Fusion and Integration for Intelligent Systems. IEEE International Conference on, pages 150-153. IEEE.
  26. Shoaib, M., Scholten, H., and Havinga, P. J. (2013). Towards physical activity recognition using smartphone sensors. In Ubiquitous Intelligence and Computing, pages 80-87. IEEE.
  27. Subramanya, A., Raj, A., Bilmes, J. A., and Fox, D. (2012). Recognizing activities and spatial context using wearable sensors. arXiv preprint arXiv:1206.6869.
  28. Wang, W.-z., Guo, Y.-w., Huang, B.-Y., Zhao, G.-r., Liu, B.- q., and Wang, L. (2011). Analysis of filtering methods for 3d acceleration signals in body sensor network. In Bioelectronics and Bioinformatics (ISBB), 2011 International Symposium on, pages 263-266. IEEE.
  29. Witten, I. H. and Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  30. Yang, J. (2009). Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics, pages 1-10. ACM.
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Paper Citation


in Harvard Style

Baumbach S. and Dengel A. (2017). Measuring the Performance of Push-ups - Qualitative Sport Activity Recognition . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 374-381. DOI: 10.5220/0006114503740381


in Bibtex Style

@conference{icaart17,
author={Sebastian Baumbach and Andreas Dengel},
title={Measuring the Performance of Push-ups - Qualitative Sport Activity Recognition},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={374-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006114503740381},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Measuring the Performance of Push-ups - Qualitative Sport Activity Recognition
SN - 978-989-758-220-2
AU - Baumbach S.
AU - Dengel A.
PY - 2017
SP - 374
EP - 381
DO - 10.5220/0006114503740381