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HUC
2007
Springer

Tracking Free-Weight Exercises

14 years 6 months ago
Tracking Free-Weight Exercises
Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. However, mechanisms for tracking free weight exercises have not yet been explored. In this paper, we study methods that automatically recognize what type of exercise you are doing and how many repetitions you have done so far. We incorporated a three-axis accelerometer into a workout glove to track hand movements and put another accelerometer on a user’s waist to track body posture. To recognize types of exercises, we tried two methods: a Naïve Bayes Classifier and Hidden Markov Models. To count repetitions developed and tested two algorithms: a peak counting algorithm and a method using the Viterbi algorithm with a Hidden Markov Model. Our experimental results showed overall recognition accuracy of around 90% over nine different exercises, and overall miscount rate of around 5%. We believe that the promising results will potentially contribute to the vision of a digital pers...
Keng-hao Chang, Mike Y. Chen, John Canny
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where HUC
Authors Keng-hao Chang, Mike Y. Chen, John Canny
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