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Volume 15
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10.3390/app15094749
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Article

Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach

by
Sergio Amat
1,
Sonia Busquier
1,
Carlos D. Gómez-Carmona
2,3,4,
Manuel Gómez-López
5,6,* and
José Pino-Ortega
3,5,6
1
Differential Equations and Numerical Analysis Research Group, Department of Applied Mathematics and Statistics, Polytechnic University of Cartagena, 30202 Cartagena, Murcia, Spain
2
Training, Physical Activity and Sports Performance Research Group (ENFYRED), Department of Music, Plastic and Body Expression, University of Zaragoza, 44003 Teruel, Aragon, Spain
3
BioVetMed & SportSci Research Group, University of Murcia, 30100 Espinardo, Murcia, Spain
4
Research Group in Optimization of Training and Sports Performance (GOERD), University of Extremadura, 10003 Caceres, Extremadura, Spain
5
Department of Physical Activity and Sport, Faculty of Sports Sciences, University of Murcia, Santiago de la Ribera, 30720 San Javier, Murcia, Spain
6
Campus of International Excellence “Mare Nostrum”, University of Murcia, 30720 San Javier, Murcia, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(9), 4749; https://doi.org/10.3390/app15094749
Submission received: 5 April 2025 / Revised: 20 April 2025 / Accepted: 23 April 2025 / Published: 25 April 2025
(This article belongs to the Section Biomedical Engineering)
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Abstract

High-intensity interval training (HIIT) is widely used in sports and health due to its cardiovascular and metabolic benefits, requiring accurate monitoring of heart rate variations to assess performance. This study proposes an automated algorithm to identify key heart rate parameters in real time, eliminating the need for manual supervision. The algorithm detects local maxima and minima in the heart rate signals recorded during HIIT sessions and calculates ascending and descending slopes, as well as intermediate averages, to evaluate cardiovascular response and recovery. The results demonstrate that the algorithm effectively identifies these parameters in all analyzed cases, providing objective insights into an athlete’s fitness level. Higher ascending slopes and lower descending slopes were associated with poorer physical condition, while a progressive increase in maxima and minima indicated proper HIIT execution and cardiovascular adaptation. This automated approach enhances performance monitoring, enabling personalized training adjustments and long-term fitness tracking. Future research should explore its applicability across different training populations and integrate additional physiological metrics.
Keywords: HIIT; heart rate; sports technology; performance analysis; sports training HIIT; heart rate; sports technology; performance analysis; sports training

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MDPI and ACS Style

Amat, S.; Busquier, S.; Gómez-Carmona, C.D.; Gómez-López, M.; Pino-Ortega, J. Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach. Appl. Sci. 2025, 15, 4749. https://doi.org/10.3390/app15094749

AMA Style

Amat S, Busquier S, Gómez-Carmona CD, Gómez-López M, Pino-Ortega J. Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach. Applied Sciences. 2025; 15(9):4749. https://doi.org/10.3390/app15094749

Chicago/Turabian Style

Amat, Sergio, Sonia Busquier, Carlos D. Gómez-Carmona, Manuel Gómez-López, and José Pino-Ortega. 2025. "Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach" Applied Sciences 15, no. 9: 4749. https://doi.org/10.3390/app15094749

APA Style

Amat, S., Busquier, S., Gómez-Carmona, C. D., Gómez-López, M., & Pino-Ortega, J. (2025). Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach. Applied Sciences, 15(9), 4749. https://doi.org/10.3390/app15094749

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