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Exercise repetition detection for resistance training based on smartphones

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Abstract

Regular exercise is one of the most important factors in maintaining a good state of health. In the past, different systems have been proposed to assist people when exercising. While most of those systems focus only on cardio exercises such as running and cycling, we exploit smartphones to support leisure activities with a focus on resistance training. We describe how off-the-shelf smartphones without additional external sensors can be leveraged to capture resistance training data and to give reliable training feedback. We introduce a dynamic time warping-based algorithm to detect individual resistance training repetitions from the smartphone’s acceleration stream. We evaluate the algorithm in terms of the number of correctly recognized repetitions. Additionally, for providing feedback about the quality of repetitions, we use the duration of an individual repetition and analyze how accurately start and end times of repetitions can be detected by our algorithm. Our evaluations are based on 3,598 repetitions performed by ten volunteers exercising in two distinct scenarios, a gym and a natural environment. The results show an overall repetition miscount rate of about 1 % and overall temporal detection error of about 11 % of individual repetition duration.

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Notes

  1. Although 3,600 exercise repetitions should have been collected, some users made a mistake counting the repetitions, therefore a slightly lower total count of usable repetitions was produced.

References

  1. ABI (2010) Mobile device user interfaces. Tech rep, ABI Research Report, Q3

  2. Ahtinen A, Isomursu M, Huhtala Y, Kaasinen J, Salminen J, Häkkilä J (2008) Tracking outdoor sports—user experience perspective. In: European conference on ambient intelligence, AmI 2008, pp 192–209

  3. Alexander JL (2002) The role of resistance exercise in weight loss. Strength Cond J 24(1):65–69

    Article  Google Scholar 

  4. Annesi JJ (1998) Effects of computer feedback on adherence to exercise. Percept Mot Skill 87(2):723–730

    Article  Google Scholar 

  5. Arzeno N, Deng ZD, Poon CS (2008) Analysis of first-derivative based QRS detection algorithms. IEEE Trans Bio Med Eng 55(2):478–484

    Article  Google Scholar 

  6. Asselin R, Ortiz G, Pui J, Smailagic A, Kissling C (2005) Implementation and evaluation of the personal wellness coach. In: 5th international workshop on smart appliances and wearable computing, ICDCSW 2005, pp 529–535

  7. Baechle T, Earle R, (U.S.), N.S..C.A. (2008) Essentials of strength training and conditioning. Human Kinetics, Champaign, IL

  8. Booth FW, Gordon SE, Carlson CJ, Hamilton MT (2000) Waging war on modern chronic diseases: primary prevention through exercise biology. J Appl Physiol 88(2):774–787

    Google Scholar 

  9. Buttussi F, Chittaro L (2008) Mopet: a context-aware and user-adaptive wearable system for fitness training. Artif Intell Med 42(2):153–163

    Article  Google Scholar 

  10. Chang KH, Chen MY, Canny J (2007) Tracking free-weight exercises. In: 9th international conference on ubiquitous computing, UbiComp 2007, pp 19–37

  11. Delavier F (2010) Strength training anatomy. Sports anatomy. Human Kinetics, Champaign, IL

    Google Scholar 

  12. Figo D, Diniz PC, Ferreira DR, Cardoso JaM (2010) Preprocessing techniques for context recognition from accelerometer data. Personal Ubiquitous Comput 14(7):645–662

    Article  Google Scholar 

  13. Fitlinxx: http://www.fitlinxx.net/. Last visited: 30 Oct 2012

  14. Fleck S, Kraemer W (2004) Designing resistance training programs. Human Kinetics, Champaign, IL

    Google Scholar 

  15. Giorgino T (2009) Computing and visualizing dynamic time warping alignments in R: the dtw package. J Stat Softw 31(7):1–24

    Google Scholar 

  16. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD explor. Newsl. 11(1)

  17. Hass CJ, Feigenbaum MS, Franklin BA (2001) Prescription of resistance training for healthy populations. Sports Med 31(14):953–964

    Article  Google Scholar 

  18. Jefit: http://www.jefit.com/. Last visited: 30 Oct 2012

  19. Liu J, Zhong L, Wickramasuriya J, Vasudevan V (2009) uWave: accelerometer-based personalized gesture recognition and its applications. Pervasive Mob Comput 5(6):657–675

    Article  Google Scholar 

  20. Mattmann C, Amft O, Harms H, Troster G, Clemens F (2007) Recognizing upper body postures using textile strain sensors. In: 11th IEEE international symposium on wearable computers, ISWC 2007, pp 1–8

  21. Melzi S, Borsani L, Cesana M (2009) The virtual trainer: supervising movements through a wearable wireless sensor network. In: 6th IEEE communications society conference on sensor and AdHoc communications and networks, SECON workshops 2009, pp 1–3

  22. Muehlbauer M, Bahle G, Lukowicz P (2011) What can an arm holster worn smart phone do for activity recognition? In: 15th annual international symposium on wearable computers, ISWC 2011, pp 79–82

  23. Muscillo R, Conforto S, Schmid M, Caselli P, D’Alessio T (2007) Classification of motor activities through derivative dynamic time warping applied on accelerometer data. In: 29th annual international conference of the IEEE engineering in medicine and biology society, EMBS 2007, pp 4930–4933

  24. Page P, Ellenbecker T (2010) Strength band training. Human Kinetics, Champaign, IL

    Google Scholar 

  25. Pedersen BK, Saltin B (2006) Evidence for prescribing exercise as therapy in chronic disease. Scand J Med Sci Sports 16(S1):3–63

    Article  Google Scholar 

  26. Pollock ML, Franklin BA, Balady GJ, Chaitman BL, Fleg JL, Fletcher B, Limacher M, Pia IL, Stein RA, Williams M, Bazzarre T (2000) Resistance exercise in individuals with and without cardiovascular disease: benefits, rationale, safety, and prescriptionan advisory from the committee on exercise, rehabilitation, and prevention, council on clinical cardiology, american heart association. Circulation 101(7):828–833

    Article  Google Scholar 

  27. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org. Last visited 30 Oct 2012

  28. Roberts CK, Barnard RJ (2005) Effects of exercise and diet on chronic disease. J Appl Physiol 98(1):3–30

    Article  Google Scholar 

  29. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech 26(1):43–49

    Article  MATH  Google Scholar 

  30. Sato K, Smith SL, Sands WA (2009) Validation of an accelerometer for measuring sport performance. J Strength Cond Res 23(1):341–347

    Article  Google Scholar 

  31. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639

    Article  Google Scholar 

  32. Schlenk EA, Dunbar-Jacob J, Sereika S, Starz T, Okifuji A, Turk D (2000) Comparability of daily diaries and accelerometers in exercise adherence in fibromyalgia syndrome. Meas Phys Educ Exerc Sci 4(2):133–134

    Google Scholar 

  33. Seeger C, Buchmann A, Van Laerhoven K (2011) myHealthAssistant: a phone-based body sensor network that captures the wearer’s exercises throughout the day. In: 6th international conference on body area networks, BodyNets 2011, pp 1–7

  34. Speck BJ, Looney SW (2001) Effects of minimal intervention to increase physical activity in women: daily activity records. Nurs Res 50(6):374–378

    Article  Google Scholar 

  35. Tormene P, Giorgino T, Quaglini S, Stefanelli M (2009) Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med 45(1):11–34

    Article  Google Scholar 

  36. Warburton DE, Nicol CW, Bredin SS (2006) Health benefits of physical activity: the evidence. Can Med Assoc J 174(6):801–809

    Article  Google Scholar 

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Acknowledgments

The work of I. Pernek has been supported by Slovenian Research Agency under grant 1000-09-310292 and by Slovene Human Resources Development and Scholarship Fund under grant 11012-34/2010. Parts of the work of K.A. Hummel have been supported by the Commission of the European Union under the FP7 Marie Curie IEF program contract PIEF-GA-2010-276336 MOVE-R.

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Correspondence to Igor Pernek.

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Pernek, I., Hummel, K.A. & Kokol, P. Exercise repetition detection for resistance training based on smartphones. Pers Ubiquit Comput 17, 771–782 (2013). https://doi.org/10.1007/s00779-012-0626-y

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