Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning - Andrea Soro; Gino Brunner; Simon Tanner; Roger Wattenhofer (Sensors, 2019)
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.