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Sensors 2019, 19(3), 714; https://doi.org/10.3390/s19030714

Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning

ETH Zurich, Department of Information Technology and Electrical Engineering, 8092 Zurich, Switzerland
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Received: 21 December 2018 / Revised: 1 February 2019 / Accepted: 5 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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Abstract

Activity recognition using off-the-shelf smartwatches is an important problem in human activity recognition. In this paper, we present an end-to-end deep learning approach, able to provide probability distributions over activities from raw sensor data. We apply our methods to 10 complex full-body exercises typical in CrossFit, and achieve a classification accuracy of 99.96%. We additionally show that the same neural network used for exercise recognition can also be used in repetition counting. To the best of our knowledge, our approach to repetition counting is novel and performs well, counting correctly within an error of ±1 repetitions in 91% of the performed sets. View Full-Text
Keywords: human activity recognition; har; smartwatch; imu; deep learning; repetition counting; exercise classification; sports analysis human activity recognition; har; smartwatch; imu; deep learning; repetition counting; exercise classification; sports analysis
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Soro, A.; Brunner, G.; Tanner, S.; Wattenhofer, R. Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning. Sensors 2019, 19, 714.

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