Exercise Recognition and Repetition Counting for Automatic Workout Documentation Using Computer Vision

Francois Volschenk, Hima Vadapalli, Dustin van der Haar

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

Abstract

This paper aims to study various approaches using deep learning methods to perform human action recognition (HAR). More specifically, a subset of HAR focused on recognising exercises and counting repetitions using deep learning. The paper discusses two approaches used in an attempt to produce a machine-learning model that is capable of identifying certain exercises from video input. This model is then incorporated into a system that can document a person’s workout by identifying the exercises being done and counting the repetitions of each exercise. The study uses artificial training data in 3D animated videos of avatars performing the exercises. The dataset used is InfiniteRep from InfinityAI. Feature extraction and repetition counting are performed using the Mediapipe pose estimation model. An LSTM-based model and a 1D time-distributed CNN are used for exercise recognition. The models were compared on classification metrics: accuracy, precision, and recall. The LSTM-based model produced a 96% accuracy on the dataset, whereas the CNN-based model produced 97.3% accuracy on the same dataset. The CNN-based model is also capable of performing in near real-time.

Original languageEnglish
Title of host publicationDigital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management - 15th International Conference, DHM 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
EditorsVincent G. Duffy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages298-309
Number of pages12
ISBN (Print)9783031610653
DOIs
Publication statusPublished - 2024
Event15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2024, held as part of the 26th HCI International Conference, HCII 2024 - Washington, United States
Duration: 29 Jun 20244 Jul 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14711 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2024, held as part of the 26th HCI International Conference, HCII 2024
Country/TerritoryUnited States
CityWashington
Period29/06/244/07/24

Keywords

  • Action Recognition
  • Computer Vision
  • Machine Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Exercise Recognition and Repetition Counting for Automatic Workout Documentation Using Computer Vision'. Together they form a unique fingerprint.

Cite this