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Automatic Exercise Recognition with Machine Learning

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Precision Health and Medicine (W3PHAI 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 843))

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Abstract

Although most individuals understand the importance of regular physical activity, many still lead mostly sedentary lives. The use of smartphones and fitness trackers has mitigated this trend some, as individuals are able to track their physical activity; however, these devices are still unable to reliably recognize many common exercises. To that end, we propose a system designed to recognize sit ups, bench presses, bicep curls, squats, and shoulder presses using accelerometer data from a smartwatch. Additionally, we evaluate the effectiveness of this recognition in a real-time setting by developing and testing a smartphone application built on top of this system. Our system recognized these activities with overall F-measures of 0.94 and 0.87 in a controlled environment and real-time setting respectively. Both users who were and who were not regularly physically active responded positively to our system, noting that our system would encourage them to continue or start exercising regularly.

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References

  1. Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In: 2010 23rd International Conference on Architecture of Computing Systems (ARCS), pp. 1–10. VDE (2010)

    Google Scholar 

  2. Bartley, J., Forsyth, J., Pendse, P., Xin, D., Brown, G., Hagseth, P., Agrawal, A., Goldberg, D.W., Hammond, T.: World of workout: a contextual mobile rpg to encourage long term fitness. In: Proceedings of the Second ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, pp. 60–67. ACM (2013)

    Google Scholar 

  3. Biddle, S.J., Mutrie, N.: Psychology of Physical Activity: Determinants, Well-being and Interventions. Routledge (2007)

    Google Scholar 

  4. Chambers, G.S., Venkatesh, S., West, G.A., Bui, H.H.: Hierarchical recognition of intentional human gestures for sports video annotation. In: Proceedings 16th International Conference on Pattern Recognition, 2002, vol. 2, pp. 1082–1085. IEEE (2002)

    Google Scholar 

  5. Cherian, J., Rajanna, V., Goldberg, D., Hammond, T.: Did you remember to brush?: a noninvasive wearable approach to recognizing brushing teeth for elderly care. In: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 48–57. ACM (2017)

    Google Scholar 

  6. Figo, D., Diniz, P.C., Ferreira, D.R., Cardoso, J.M.: Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquitous Comput. 14(7), 645–662 (2010)

    Article  Google Scholar 

  7. Franco, O.H., de Laet, C., Peeters, A., Jonker, J., Mackenbach, J., Nusselder, W.: Effects of physical activity on life expectancy with cardiovascular disease. Arch. Intern. Med. 165(20), 2355–2360 (2005)

    Article  Google Scholar 

  8. Godin, G., Shephard, R., et al.: A simple method to assess exercise behavior in the community. Can. J. Appl. Sport. Sci. 10(3), 141–146 (1985)

    Google Scholar 

  9. Goodwin, R.D.: Association between physical activity and mental disorders among adults in the united states. Prev. Med. 36(6), 698–703 (2003)

    Article  Google Scholar 

  10. Guthold, R., Stevens, G.A., Riley, L.M., Bull, F.C.: Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob. Health 6(10), e1077–e1086 (2018)

    Article  Google Scholar 

  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  12. Kowsar, Y., Moshtaghi, M., Velloso, E., Kulik, L., Leckie, C.: Detecting unseen anomalies in weight training exercises. In: Proceedings of the 28th Australian Conference on Computer-Human Interaction, pp. 517–526. ACM (2016)

    Google Scholar 

  13. Morris, D., Saponas, T.S., Guillory, A., Kelner, I.: Recofit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3225–3234. ACM (2014)

    Google Scholar 

  14. Mortazavi, B.J., Pourhomayoun, M., Lee, S.I., Nyamathi, S., Wu, B., Sarrafzadeh, M.: User-optimized activity recognition for exergaming. Pervasive Mob. Comput. 26, 3–16 (2016)

    Article  Google Scholar 

  15. Mortazavi, B.J., Pourhomayoun, M., Alsheikh, G., Alshurafa, N., Lee, S.I., Sarrafzadeh, M.: Determining the single best axis for exercise repetition recognition and counting on smartwatches. In: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 33–38. IEEE (2014)

    Google Scholar 

  16. Organization, W.H.: Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. World Health Organization (2009)

    Google Scholar 

  17. Organization, W.H., et al.: Global Recommendations on Physical Activity for Health. World Health Organization (2010)

    Google Scholar 

  18. Paffenbarger Jr, R.S., Hyde, R., Wing, A.L., Hsieh, C.C.: Physical activity, all-cause mortality, and longevity of college alumni. N. Engl. J. Med. 314(10), 605–613 (1986)

    Article  Google Scholar 

  19. Pernek, I., Kurillo, G., Stiglic, G., Bajcsy, R.: Recognizing the intensity of strength training exercises with wearable sensors. J. Biomed. Inform. 58, 145–155 (2015)

    Article  Google Scholar 

  20. Pruthi, D., Jain, A., Jatavallabhula, K.M., Nalwaya, R., Teja, P.: Maxxyt: An autonomous wearable device for real-time tracking of a wide range of exercises. In: 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), pp. 137–141. IEEE (2015)

    Google Scholar 

  21. Rajanna, V., Lara-Garduno, R., Behera, D.J., Madanagopal, K., Goldberg, D., Hammond, T.: Step up life: a context aware health assistant. In: Proceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, pp. 21–30. ACM (2014)

    Google Scholar 

  22. Rhodes, R.E., Plotnikoff, R.C., Courneya, K.S.: Predicting the physical activity intention-behavior profiles of adopters and maintainers using three social cognition models. Ann. Behav. Med. 36(3), 244–252 (2008)

    Article  Google Scholar 

  23. Shen, C., Ho, B.J., Srivastava, M.: Milift: Efficient smartwatch-based workout tracking using automatic segmentation. IEEE Trans. Mob. Comput. 17(7), 1609–1622 (2018)

    Article  Google Scholar 

  24. Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 37–40. IEEE (2007)

    Google Scholar 

  25. Um, T.T., Babakeshizadeh, V., Kulić, D.: Exercise motion classification from large-scale wearable sensor data using convolutional neural networks. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2385–2390. IEEE (2017)

    Google Scholar 

  26. Weiss, G.M., Timko, J.L., Gallagher, C.M., Yoneda, K., Schreiber, A.J.: Smartwatch-based activity recognition: A machine learning approach. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 426–429. IEEE (2016)

    Google Scholar 

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Correspondence to Josh Cherian .

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Mendiola, V. et al. (2020). Automatic Exercise Recognition with Machine Learning. In: Shaban-Nejad, A., Michalowski, M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-030-24409-5_4

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