Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

This study aimed to examine the accuracy of shots taken at various heart rate (HR) percentages resulting from increasing workload. Twenty-one male football players (age: 21.7 years) in amateur teams participated in the study. Athletes were asked to shoot at the center of the prepared target ten meters away before the test and at each level of the progressively increasing intensity exercise protocol (Yo-Yo intermittent recovery test level 1 (Yo-Yo IR1)). At the end of each level, the distance of the shots to the center of the target and the areas of contact with the target was determined. During the test, HR was monitored and percentages of maximum HR (%HRmax) at each level were determined. The distance to the target and contact zones of the shots were compared depending on different percentages of %HRmax. It was observed that the increased HR due to the increased workload negatively affected the accuracy of the shots. In particular, the accuracy rate decreased significantly after 80% of the maximum HR compared with the resting HR (47-82%). Additionally, it was observed that the percentage of balls directed to the upper right corner increased as HR rose, with no shots targeting the center after the third level of Yo-Yo IR1 (%HRmax=~80%). Therefore, it can be concluded that a high HR impairs football accuracy. Consequently, football players should engage in workouts that enhance their ability to perform at a high HR, and coaches should design training programs in this vein.

Background and aim of the study : In recent years, the use of ergogenic aid has become widespread in order to improve performance among the athletes and to achieve success more easily. Pre-workout nutritional supplements that attract the attention of many athletes as a legal ergogenic aid are used by both aerobic and anaerobic branch athletes. The aim of this study is to examine the acute effect of the pre-workout supplement on aerobic and anaerobic performance in basketball players. Material and Method: Twenty male college basketball players who have played regularly at least for 5 years participated in the study voluntarily (mean ± SD 22.00 ± 1.70 years, height 1.83 ± 0.07 cm, weight 85.15 ± 10.78 kg, BMI 25.32 ± 3.10). Double-blind placebo-controlled, cross-over method was used for the collection of data. The players were divided into two groups as placebo (PL, n= 10) and pre-workout (PRW, n= 10). Placebo and pre-workout groups were replaced 48 hours after the initial measurements and the same tests were repeated. The pre-workout group was given 17 gr (Bigjoy Predator) in 200 mg of additive-free juice 1 hour before the test, only 200 mg of additive free juice was given to the placebo group. Countermovement Jump (CMJ) and Running-Based Anaerobic Sprint Test (RAST) were used to measure the anaerobic performance of the athletes, and Yoyo Intermittent Recovery Test 1(Yo-Yo IRL1) was used to measure aerobic capacities. Results: A significant difference was found concerning the Countermovement Jump (CMJ) test results (p= 0, 004), Relative peak power (p= 0.001) and Relative average power (p= 0.012) values obtained from RAST test data. There was no significant difference in fatigue index (p = 0.79) and VO2 max (p= 0.492) values. Conclusion: While pre-workout supplementation has an acute effect on anaerobic power data, it has been observed that there is no acute effect on the data obtained from fatigue index and aerobic endurance test.

Accurately estimating physical fatigue is critical for preventing injuries, enhancing performance and supporting rehabilitation, yet current approaches are often limited to specific tasks with no interpretability. In this work, we present a systematic framework for predicting perceived fatigue in upper limb movements using wearable inertial measurement units (IMU) and surface electromyography (EMG). We processed two publicly available datasets into structured repetition level data with extracting over 3,700 biomechanical features across time, frequency and time–frequency domains. We model perceived fatigue using the Borg Rating of Perceived Exertion (RPE) scale. A six-level hierarchical evaluation was designed to progressively test models’ performance and generalisation, from single-task conditions to cross-family transfer. The results show that the models achieve the highest accuracy in task-specific contexts (R² to 0.69), but the performance declines with increasing task diversity, with cross-family transfer resulting in a negative R². Fewshot domain adaptation significantly improved performance by turning negative R2 into positive values across all family combinations. Feature importance analyses found that task specific models relied more heavily on IMU based kinematic features, while EMG features grew in importance as models generalised. Sensor reduction experiments showed that close to optimal accuracy can be achieved with as few as four to six sensors compared to eleven sensors for the full dataset. This study provides a unified methodology for fatigue estimation, exposes the challenges of cross-task generalisation and provides guidelines for sensor configurations that balance performance with minimal burden.

Background and Study Aim. The aim of this study is to examine the electromyographic responses to Nordic curl and prone leg curl exercises, having two different mechanics. Material and Methods. The athletes performed the prone leg curl and Nordic curl exercises in random order, 6 repetitions each. Electromyographic data of semimemranosus, semitendinosus, biceps femoris and rectus femoris muscles were recorded by 8-channel electromyography in order to examine the muscle responses to exercises. Total duration of exercise, cumulative integrated electromyographic values and muscle activation rates in 5 different intensity zones determined according to MVC% values have been compared. Results. Prone leg curl exercise occurred in less time than Nordic curl exercise. According to the cumulative integrated electromyography data results, all muscles showed similar muscle activation in both exercises. Comparing the muscle activation rates in the five intensity zones, more muscle activity was observed for Nordic curl exercise in the first intensity zone, while prone leg curl exercise was more active in the third and fourth zones. During the prone leg curl exercise, the muscle activation rate of the dominant leg is higher in the first intensity zone, whereas the non-dominant leg in the fourth intensity zone has a higher muscle activation. During the Nordic curl exercise, the muscle activation rates of the dominant leg in the first and fifth intensity zones are higher, whereas the nondominant leg in the fourth intensity zone is higher. Conclusions. Prone leg curl exercises can be preferred in order to stimulate high muscle activation in a short time. Comparing the two exercises there was no significant difference in muscle activity in dominant and nondominant legs.

Accurately estimating perceived fatigue from wearable sensor data is a challenge, especially across diverse tasks. This letter presents a generalized framework to predict estimated fatigue scores (measured using the Borg scale) using combined electromyography and inertial measurement units data collected from two independent upper limb datasets. Our best model achieved a mean absolute error of 2.35 and a mean absolute percentage error of 18.60% using only five strategically placed sensors. A broad set of biomechanical features was extracted to capture both kinematic and neuromuscular indicators of fatigue. Vertical acceleration of the upper arm and shoulder, along with spectral features from deltoid EMG, emerged as the most consistent predictors across tasks. These findings support interpretable and generalizable fatigue detection and provide a foundation for real-time monitoring systems in sports, rehabilitation, and occupational health.