# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 5 Conclusions  
The automated algorithm developed in this study provides a reliable, efficient method for identifying key heart rate parameters during HIIT sessions without requiring manual supervision. The successful detection of maxima, minima, ascending and descending slopes, and intermediate averages offers valuable insights into cardiovascular responses to high-intensity interval training. The results demonstrate that well-executed HIIT sessions show a progressive increase in both maxima and minima, while the slope calculations serve as objective metrics for assessing cardiovascular fitness levels. This automated approach enhances objectivity in performance monitoring and enables real-time analysis of training responses.  
From a practical perspective, this algorithm offers several advantages for practitioners, coaches, and healthcare providers implementing HIIT protocols. The automated analysis eliminates the need for a time-consuming manual review of heart rate data while maintaining high accuracy and consistency. Coaches can use the derived metrics to personalize training prescriptions based on individual cardiovascular responses, optimize work-to-rest ratios, and track progress over time. For participants, real-time feedback on heart rate dynamics can improve pacing strategies and help maintain appropriate intensity zones during HIIT sessions. In rehabilitation or health contexts, the objective quantification of cardiovascular strain and recovery efficiency can enhance safety and effectiveness when implementing HIIT with clinical populations. Future applications could integrate this algorithm into wearable technology and training software, making sophisticated heart rate analysis accessible in field settings without specialized equipment or expertise.  
Conceptualization, S.A. and S.B.; methodology, S.A., S.B. and C.D.G.-C.; software, S.A. and S.B.; validation, C.D.G.-C., M.G.-L. and J.P.-O.; formal analysis, S.A., S.B. and C.D.G.-C.; investigation, C.D.G.-C. and M.G.-L.; resources, J.P.-O. and M.G.-L.; data curation, C.D.G.-C.; writing--original draft preparation, S.A. and S.B.; writing--review and editing, C.D.G.-C., [PERSON] and [PERSON]-O.; visualization, S.A. and C.D.G.-C.; supervision, [PERSON]-L. and [PERSON]-[PERSON]; project administration, J.P.-O.; funding acquisition, J.P.-O. All authors have read and agreed to the published version of the manuscript.  
This research has not received external funding.  
The study protocol was approved by the Bioethics Committee of the University of Murcia (reference number: 3495/2021) and conducted in accordance with the Declaration of Helsinki.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This research was carried out with the collaboration of the University Chair in Physical Activity, Sport, and Health and the support of the Cartagena City Council. Furthermore, it derives from the doctoral thesis of [PERSON] entitled \"Development and Application of Mathematical Algorithms Applied to the Monitoring of Physical Exercise and Sports Training\", completed within the framework of the PhD program in Physical Activity and Sport Sciences at the University of Murcia.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.