Detection of Delayed Onset Muscle Soreness (DOMS) in Static Cycling Exercise Using Multilayer Perceptron Neural Network (MLPNN)

Nathaniel Win Lincoln*, Rachmad Setiawan, Nada Fitrieyatul Hikmah

*Corresponding author for this work

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

Abstract

Static cycling is a convenient and widely accessible form of exercise, with spinning being a popular indoor cycling workout using specialized stationary bikes. However, spinning comes with some risks, including the potential for exertional rhabdomyolysis (ER), known explicitly as spinning-induced ER (SIER). There have been 46 reported cases of SIER, which involves the breakdown of muscle tissue and can lead to symptoms like fatigue, nausea, muscle pain, and organ dysfunction. Detecting Delayed Onset Muscle Soreness (DOMS), one of the symptoms of rhabdomyolysis, can help diagnose SIER early. To achieve this, a study was conducted using Electromyograph (EMG) sensors placed on the quadriceps, hamstrings, and calves muscles of healthy young male subjects (20-22 years old) during sprint interval cycling (SIC) exercise. The EMG signals were analyzed in time and frequency domains to extract relevant parameters. Subjective testing was also done using the Likert Scale of Muscle Soreness questionnaire as a reference. The study proposed a Multilayer Perceptron Neural Network (MLPNN) classification method for DOMS detection, achieving an impressive accuracy of 94.2% and a loss value of 6.7%. These findings indicate the system's effectiveness in objectively identifying DOMS in individuals participating in static bicycle exercise.

Original languageEnglish
Title of host publication2023 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages147-152
Number of pages6
ISBN (Electronic)9798350302424
DOIs
Publication statusPublished - 2023
Event3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023 - Hybrid, Yogyakarta, Indonesia
Duration: 9 Nov 202310 Nov 2023

Publication series

Name2023 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

Conference

Conference3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period9/11/2310/11/23

Keywords

  • DOMS
  • EMG
  • MLPNN
  • rhabdomyolysis
  • static cycling

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