Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Abstract

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.

Enhanced cardiac vagal tone in mental fatigue: Analysis of heart rate variability in Time-on-Task, recovery, and reactivity

“…In addition, frequent episodes of acute mental fatigue may increase the risk of developing more chronic forms of fatigue. For these reasons, many technical systems and analytic approaches have been developed to monitor the physiological manifestations of mental fatigue including plenty of studies that suggest that HRV may be particularly suited as a marker of fatigue [ 75 – 78 ]. The current study contributes to this field by suggesting that among the many HRV components, it may especially be the vagal-mediated components of the HRV spectrum that are reliable physiological indicators of operators’ fatigue in different work context.…”

Game Plan: What AI can do for Football, and What Football can do for AI

“…Finally, recent developments in computer vision have been employed for player tracking (Bialkowski et al, 2015;Gade & Moeslund, 2018;Liu, Carr, Collins, & Liu, 2013;Lu, Ting, Little, & Murphy, 2013), pose estimation (Bridgeman, Volino, Guillemaut, & Hilton, 2019;Fastovets, Guillemaut, & Hilton, 2013;Sypetkowski, Kurzejamski, & Sarwas, 2019;Sypetkowski, Sarwas, & Trzcinski, 2019;F. Zhang, Zhu, & Ye, 2019), and automated injury prediction (Kampakis, 2016) based on, e.g., gait and fatigue analysis (Bartlett, 2006;Claudino et al, 2019;Kakavas, Malliaropoulos, Pruna, & Maffulli, 2019;Op De Beéck, Meert, Schütte, Vanwanseele, & Davis, 2018;Ramos et al, 2020).…”

A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

“…Their findings showed that kinematic changes were related to fatigue for all runners, and fatigue was dependent on participants’ running technique [ 20 ]. To date, there are only a few studies focusing on continuous movement changes induced by fatigue, e.g., [ 1 , 20 , 21 ]. Ramos et al presented a machine learning system to evaluate fatigue using electromyographic (EMG) and heart rate variability (HRV) measurements [ 21 ].…”

“…To date, there are only a few studies focusing on continuous movement changes induced by fatigue, e.g., [ 1 , 20 , 21 ]. Ramos et al presented a machine learning system to evaluate fatigue using electromyographic (EMG) and heart rate variability (HRV) measurements [ 21 ]. The approach showed a potential to implement a combination of a dimensionless (0–1) global fatigue descriptor to reflect the onset of fatigue.…”

“…However, these models can only represent a simplistic process of fatigue development, and an essential early intervention is impossible before the athlete is deemed to be fatigued. Limited research [ 1 , 20 , 21 ] has been conducted to monitor gradual and continuous changes in fatigue levels. The change from low levels of fatigue to high levels of fatigue is a continuous process, which may take place gradually, and the variations of human motion due to fatigue are fairly small during exercise, making it difficult to detect continuous changes in fatigue [ 17 ].…”