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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Aalto University

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.

Details

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Work Conference on Artificial Neural Networks - Gran Canaria, Spain
Duration: 12 Jun 201914 Jun 2019
Conference number: 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Work Conference on Artificial Neural Networks
Abbreviated titleIWANN
CountrySpain
CityGran Canaria
Period12/06/201914/06/2019

    Research areas

  • Acceleration, Activity recognition, CNN, Deep learning, Movesense, Neural Networks, Sensors, Workout

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