# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
###### 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. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
synchronize with physiological sensors that measure internal load parameters, such as heart rate [8; 9; 10]. High-intensity interval training (HIIT) has become increasingly prominent in both athletic training regimens and general health applications due to its demonstrated efficiency in improving cardiovascular fitness and metabolic health [11; 12]. HIIT consists of alternating periods of high-intensity exercise performed at near-maximal effort (typically 80-95% of maximum heart rate), interspersed with periods of lower-intensity recovery or complete rest [13]. The high-intensity intervals generally range from 10 to 30 s, though they can vary depending on the participant's fitness level, with the total session duration typically between 18 and 30 min [14]. While recovery periods are incorporated between high-intensity exercise bouts, the duration and completeness of recovery vary significantly across different HIIT protocols, with some allowing full recovery and others deliberately incorporating incomplete recovery as part of the training stimulus [14]. This training modality has shown substantial benefits across diverse populations, including improvements in body composition, cardiorespiratory fitness, insulin sensitivity, and even cognitive function and psychological well-being [15; 16; 17; 18]. Despite its effectiveness, proper implementation of HIIT requires precise monitoring of physiological responses, particularly heart rate dynamics, to ensure that intensity targets are met and that adequate recovery occurs between intervals [14]. Heart rate monitoring during HIIT provides crucial information about cardiovascular strain and recovery capacity, which directly informs training prescription and adaptation assessment [19]. Traditional approaches to HIIT monitoring have relied on manual observation and the interpretation of heart rate data by coaches or sports scientists, which is time-consuming, potentially inconsistent, and not scalable for large groups or remote training settings [20; 21]. Exercise specialists remain essential both for monitoring physical and physiological responses during HIIT, so automated heart rate detection helps them to provide more precise supervision and focus on correct execution and injury prevention during training. Examining heart rate signals during HIIT sessions reveals characteristic patterns of peaks (maximum) during high-intensity intervals and valleys (minimum) during recovery phases [14; 22]. These data points provide valuable insights into cardiac responses to high-intensity exercise and the efficiency of recovery between intervals.