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A Cycling Movement Based System for Real-Time Muscle Fatigue and Cardiac Stress Monitoring and Analysis

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  • Szi-Wen Chen
  • Jiunn-Woei Liaw
  • Ya-Ju Chang
  • Hsiao-Lung Chan
  • Li-Yu Chiu

Abstract

In this study, we defined a new parameter, referred to as the cardiac stress index (CSI), using a nonlinear detrended fluctuation analysis (DFA) of heart rate (HR). Our study aimed to incorporate the CSI into a cycling based fatigue monitoring system developed in our previous work so the muscle fatigue and cardiac stress can be both continuously and quantitatively assessed for subjects undergoing the cycling exercise. By collecting electrocardiogram (ECG) signals, the DFA scaling exponent α was evaluated on the RR time series extracted from a windowed ECG segment. We then obtained the running estimate of α by shifting a one-minute window by a step of 20 seconds so the CSI, defined as the percentage of all the less-than-one α values, can be synchronously updated every 20 seconds. Since the rating of perceived exertion (RPE) scale is considered as a convenient index which is commonly used to monitor subjective perceived exercise intensity, we then related the Borg RPE scale value to the CSI in order to investigate and quantitatively characterize the relationship between exercise-induced fatigue and cardiac stress. Twenty-two young healthy participants were recruited in our study. Each participant was asked to maintain a fixed pedaling speed at a constant load during the cycling exercise. Experimental results showed that a decrease in DFA scaling exponent α or an increase in CSI was observed during the exercise. In addition, the Borg RPE scale and CSI were positively correlated, suggesting that the factors due to cardiac stress might also contribute to fatigue state during physical exercise. Since the CSI can effectively quantify the cardiac stress status during physical exercise, our system may be used in sports medicine, or used by cardiologists who carried out stress tests for monitoring heart condition in patients with heart diseases.

Suggested Citation

  • Szi-Wen Chen & Jiunn-Woei Liaw & Ya-Ju Chang & Hsiao-Lung Chan & Li-Yu Chiu, 2015. "A Cycling Movement Based System for Real-Time Muscle Fatigue and Cardiac Stress Monitoring and Analysis," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-17, June.
  • Handle: RePEc:plo:pone00:0130798
    DOI: 10.1371/journal.pone.0130798
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    References listed on IDEAS

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    1. Rodriguez, Eduardo & Echeverria, Juan C. & Alvarez-Ramirez, Jose, 2007. "Detrended fluctuation analysis of heart intrabeat dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 429-438.
    2. Absil, P.-A & Sepulchre, R & Bilge, A & Gérard, P, 1999. "Nonlinear analysis of cardiac rhythm fluctuations using DFA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 235-244.
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    Cited by:

    1. Giovanna Zimatore & Maria Chiara Gallotta & Matteo Campanella & Piotr H. Skarzynski & Giuseppe Maulucci & Cassandra Serantoni & Marco De Spirito & Davide Curzi & Laura Guidetti & Carlo Baldari & Stavr, 2022. "Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    2. Li-Ling Chuang & Yu-Fen Chuang & Miao-Ju Hsu & Ying-Zu Huang & Alice M K Wong & Ya-Ju Chang, 2018. "Validity and reliability of the Traditional Chinese version of the Multidimensional Fatigue Inventory in general population," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-18, May.

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