Exercise repetition detection for resistance training based on smartphones

  • Authors:

  • Igor Pernek;Karin Anna Hummel;Peter Kokol

  • Affiliations:

  • Faculty of Health Sciences, Research Institute, University of Maribor, Maribor, Slovenia 2000;Computer Engineering and Networks Laboratory, Communication Systems Group, ETH Zürich, Zurich, Switzerland 8092;Faculty of Health Sciences, Research Institute, University of Maribor, Maribor, Slovenia 2000

  • Venue:
  • Personal and Ubiquitous Computing
  • Year:
  • 2013

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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.