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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.

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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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Authors and Affiliations

  1. Faculty of Health Sciences, Research Institute, University of Maribor, Zitna ulica 15, 2000, Maribor, Slovenia

    Igor Pernek & Peter Kokol

  2. Computer Engineering and Networks Laboratory, Communication Systems Group, ETH Zürich, Gloriastrasse 35, 8092, Zurich, Switzerland

    Karin Anna Hummel

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  1. Igor Pernek
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  2. Karin Anna Hummel
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  3. Peter Kokol
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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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