Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu


  • Kacper Skawinski
  • Ferran Montraveta Roca
  • Rainhard Dieter Findling
  • Stephan Sigg


  • Aalto University


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.


OtsikkoAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
ToimittajatIgnacio Rojas, Gonzalo Joya, Andreu Catala
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Work Conference on Artificial Neural Networks - Gran Canaria, Espanja
Kesto: 12 kesäkuuta 201914 kesäkuuta 2019
Konferenssinumero: 15


NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta11506 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349


ConferenceInternational Work Conference on Artificial Neural Networks
KaupunkiGran Canaria

Lataa tilasto

Ei tietoja saatavilla

ID: 35740669