# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### Wearable Devices  
Recent advancements in wearable sensor technology [8;9], especially in the field of human-machine interaction, are increasingly aligned with the needs of clinical monitoring, including the early detection of cardiac fatigue [10]. While traditional wearable systems were limited by their low integration and measurement accuracy, emerging innovations have addressed these gaps through compact system designs, flexible biosensors and improved battery life. Notably, the integration of electrochemical sensors allows for the simultaneous detection of multiple biomarkers (such as sweat analysis) in non-invasive modes, offering a pain-free alternative to traditional methods. Coupled with modern machine learning (ML) algorithms, these systems now enable the indirect estimation of physiological states, including those relevant to cardiac performance [11]. These trends underscore a shift toward more smart, high-fidelity and clinically relevant wearable mon itoring platforms that are highly compatible with the early detection of conditions like cardiac fatigue.  
Some studies reported that strenuous endurance training may result in both skeletal and cardiac muscle fatigue [12; 13]. This condition is called \"exercise-induced cardiac fatigue\" (EICF), and it manifests as a brief decrease in left (LV) or right (RV) ventricular function in otherwise healthy individuals [14; 15]. While traditional echocardiographic metrics like LV ejection fraction (LVEF) can be used to evaluate EICF, myocardial deformation imaging--that is, global longitudinal strain measured by speckle tracking echocardiography--is becoming a more accurate and sensitive metric [16; 17]. These studies tried to evaluate the occurrence of EICF in recreational ultramarathon runners and to ascertain potential predictive factors of myocardial dysfunction (as ventricular tachycardia and sudden cardiac death), but this type of activity is very far from those we found in the human activity recognition (HAR) context.  
The detection of different human actions or gestures, as well as additional in-depth information about human activities (such as the quality of motions, emotions and gender) based on sensor data, are all parts of the large research topic known as HAR. The advent of wearable, inexpensive, low-power sensors and live data streaming has led to the application of numerous innovative data processing and analysis techniques to HAR in recent years.  
Molecular biomarkers are substances detectable in the blood or other body fluids that indicate cardiovascular stress or damage. [PERSON] et al. (2024) [18] observed that cardiovascular biomarkers can increase, and heart function may temporarily deteriorate, as a result of ultra-endurance event participation. For most molecular biomarkers, a blood sample is required.  
The advantage of physiological indicators like heart rate (HR), is that they can be recorded and analyzed remotely. Moreover, they can be used for an initial screening to identify an at-risk subject, who can subsequently undergo advanced diagnostic tests and blood tests for molecular biomarkers [19]. Recently, there has been a significant proliferation of wearable devices such as smartwatches, rings and chest bands in cardiac care [20].