Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest
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A4 Artikkeli konferenssijulkaisussa
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en
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13
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Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings, pp. 347-359, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 11506 LNCS
Abstract
Sports and workout activities have become important parts of modern life. Nowadays, many people track characteristics about their sport activities with their mobile devices, which feature inertial measurement unit (IMU) sensors. In this paper we present a methodology to detect and recognize workout, as well as to count repetitions done in a recognized type of workout, from a single 3D accelerometer worn at the chest. We consider four different types of workout (pushups, situps, squats and jumping jacks). Our technical approach to workout type recognition and repetition counting is based on machine learning with a convolutional neural network. Our evaluation utilizes data of 10 subjects, which wear a Movesense sensors on their chest during their workout. We thereby find that workouts are recognized correctly on average 89.9% of the time, and the workout repetition counting yields an average detection accuracy of 97.9% over all types of workout.Description
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Skawinski, K, Montraveta Roca, F, Findling, R D & Sigg, S 2019, Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest. in I Rojas, G Joya & A Catala (eds), Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11506 LNCS, Springer, pp. 347-359, International Work Conference on Artificial Neural Networks, Gran Canaria, Spain, 12/06/2019. https://doi.org/10.1007/978-3-030-20521-8_29