Work

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

Year: 2025

Type: article

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 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.

Source: Applied Sciences

DOI: 10.3390/app15094749

Authors: Sergio Amat, Sonia Busquier, Carlos D. Gómez-Carmona, Manuel Gómez-López, José Pino-Ortega

Institutions: Universidad Politécnica de Cartagena, Universidad de Zaragoza, Universidad de Murcia, Universidad de Extremadura

Language: English

Topic: Cardiovascular and exercise physiology

Subfield: Complementary and alternative medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896