Activity Recognition of Local Muscular Endurance (LME) Exercises Using an Inertial Sensor

Ghanashyama Prabhu, Amin Ahmadi, Noel E. O’Connor, Kieran Moran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose an algorithmic approach for a motion analysis framework to automatically recognize local muscular endurance (LME) exercises and to count their repetitions using a wrist-worn inertial sensor. LME exercises are prescribed for cardiovascular disease rehabilitation. As a technical solution, we propose activity recognition based on machine learning. We developed an algorithm to automatically segment the captured data from all participants. Relevant time and frequency domain features were extracted using a sliding window technique. Principal component analysis (PCA) was applied for dimensionality reduction of the extracted features. We trained 15 binary classifiers using support vector machine (SVM) to recognize individual LME exercises, achieving overall accuracy of more than 98%. We applied grid search technique to obtain the optimal SVM hyperplane parameters. The learning curves (mean ± stdev) for each model is investigated to verify that the models were not over-fitted and performed well on any new test data. Also, we devised a method to count the repetitions of the upper body exercises.

Original languageEnglish
Title of host publicationProceedings of the 11th International Symposium on Computer Science in Sport, IACSS 2017
EditorsDietmar Saupe, Martin Lames, Josef Wiemeyer
PublisherSpringer Verlag
Pages35-47
Number of pages13
ISBN (Print)9783319678450
DOIs
Publication statusPublished - 2018
Event11th International Symposium on Computer Science in Sport, IACSS 2017 - Konstanz, United Kingdom
Duration: 06-09-201709-09-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume663
ISSN (Print)2194-5357

Conference

Conference11th International Symposium on Computer Science in Sport, IACSS 2017
Country/TerritoryUnited Kingdom
CityKonstanz
Period06-09-1709-09-17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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