Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review

Fatigue Affects Body Acceleration During Vertical Jumping and Agility Tasks in Elite Young Badminton Players

BACKGROUND

Badminton is a sport demanding both high aerobic and anaerobic fitness levels, and fatigue can significantly impact game performance. However, relevant studies are limited, and none have employed a wearable inertial measurement unit (IMU) to investigate the effects of fatigue on athletic performance in the field.

HYPOTHESIS

Overall performance and body acceleration in both time and frequency domains during the fundamental badminton skills of vertical jumping and changes of direction will be affected by fatigue.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

A total of 38 young badminton players competing at the Division I level participated. Body accelerations while performing vertical jump and agility-T tests before and immediately after undergoing a fatigue protocol were measured by an IMU, positioned at the L4 to L5 level.

RESULTS

Jumping height decreased significantly by 4 cm (P < 0.01) after fatigue with greater downward acceleration (1.03 m/s2, P < 0.05) during the squatting subphase. Finishing time increased significantly by 50 ms only during the 10-m side-shuffling of the agility-T test (P = 0.02) after fatigue with greater peak and mean accelerations (3.83 m/s2, P = 0.04; 0.43 m/s2, P < 0.01), and higher median and mean frequency (0.38 Hz, P = 0.04, 0.11 Hz, P = 0.01).

CONCLUSION

This study using a wearable IMU demonstrates the effects of fatigue on body acceleration in badminton players. The frequency-domain analysis further indicated that fatigue might lead to loss of voluntary control of active muscles and increased impacts on the passive elastic elements.

CLINICAL RELEVANCE

The findings imply that fatigue can lead to diminished athletic performance and highlight the potential for an increased risk of sports injuries. Consequently, maintaining precision in monitoring fatigue is crucial for elite young badminton players.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.

Evaluation of the Vmaxpro sensor for assessing movement velocity and load-velocity variables: accuracy and implications for practical use

We investigated the ecological validity of an inertial measurement unit (IMU) (Vmaxpro) to assess the movement velocity (MV) during a 1-repetition maximum (1RM) test and for the prediction of load-velocity (L-V) variables, as well as the ecological intra- day and inter-day reliability during free-weight bench press (BP) and squat (SQ). Furthermore, we provide recommendations for the practical use of the sensor. Twenty-three strength-trained men completed an incremental 1RM test, whereas seventeen men further participated in another 3 sessions consisting of 3 repetitions with 4 different loads (30, 50, 70 and 90% of 1RM) to assess validity and intra- and inter-day reliability, respectively. The MV was assessed using the Vmaxpro and a 3D motion capture system (MoCap). L-V variables and the 1RM were calculated based on submaximal velocities. The Vmaxpro showed high validity during the 1RM test for BP (r = 0.935) and SQ (r = 0.900), but with decreasing validity at lower MVs. The L-V variables and the 1RM demonstrated high validity for BP (r = 0.808-0.942) and SQ (r = 0.615-0.741) with a systematic overestimation. Coefficients of variance for intra- and inter-day reliability ranged from 2.4% to 9.7% and from 3.2% to 8.6% for BP and SQ, respectively. The Vmaxpro appears valid at high and moderately valid at low MVs. Depending on the required degree of accuracy, the sensor may be sufficient for the prediction of L-V variables and the 1RM. Our data indicate the sensor to be suitable for monitoring changes in MVs within and between training sessions.

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.

Velocity-Based Strength Training: The Validity and Personal Monitoring of Barbell Velocity with the Apple Watch

Velocity-based training (VBT) is a method to monitor resistance training based on measured kinematics. Often, measurement devices are too expensive for non-professional use. The purpose of this study was to determine the accuracy and precision of the Apple Watch 7 and the Enode Pro device for measuring mean, peak, and propulsive velocity during the free-weighted back squat (in comparison to Vicon as the criterion). Velocity parameters from Vicon optical motion capture and the Apple Watch were derived by processing the motion data in an automated Python workflow. For the mean velocity, the barbell-mounted Apple Watch (r = 0.971-0.979, SEE = 0.049), wrist-worn Apple Watch (r = 0.952-0.965, SEE = 0.064) and barbell-mounted Enode Pro (r = 0.959-0.971, SEE = 0.059) showed an equal level of validity. The barbell-mounted Apple Watch (Vpeak: r = 0.952-0.965, SEE = 0.092; Vprop: r = 0.973-0.981, SEE = 0.05) was found to be the most valid for assessing propulsive and peak lifting velocity. The present results on the validity of the Apple Watch are very promising, and may pave the way for the inclusion of VBT applications in mainstream consumer wearables.

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.

Development and Evaluation of a Full-Waveform Resistance Training Monitoring System Based on a Linear Position Transducer

Recent advances in training monitoring are centered on the statistical indicators of the concentric phase of the movement. However, those studies lack consideration of the integrity of the movement. Moreover, training performance evaluation needs valid data on the movement. Thus, this study presents a full-waveform resistance training monitoring system (FRTMS) as a whole-movement-process monitoring solution to acquire and analyze the full-waveform data of resistance training. The FRTMS includes a portable data acquisition device and a data processing and visualization software platform. The data acquisition device monitors the barbell's movement data. The software platform guides users through the acquisition of training parameters and provides feedback on the training result variables. To validate the FRTMS, we compared the simultaneous measurements of 30-90% 1RM of Smith squat lifts performed by 21 subjects with the FRTMS to similar measurements obtained with a previously validated three-dimensional motion capture system. Results showed that the FRTMS produced practically identical velocity outcomes, with a high Pearson's correlation coefficient, intraclass correlation coefficient, and coefficient of multiple correlations and a low root mean square error. We also studied the applications of the FRTMS in practical training by comparing the training results of a six-week experimental intervention with velocity-based training (VBT) and percentage-based training (PBT). The current findings suggest that the proposed monitoring system can provide reliable data for refining future training monitoring and analysis.

Comparison of the PUSH Band 2.0 and Vicon Motion Capture to Measure Concentric Movement Velocity during the Barbell Back Squat and Bench Press

The purpose of this investigation was to compare concentric movement velocity (CMV) measured with the PUSH Band (v2.0) and a Vicon motion capture system (MC) during the back squat (SQ) and the bench press (BP) resistance exercises (RE). Twelve resistance-trained males (26.0 ± 5.5 years; 175.6 ± 4.9 cm; 96.3 ± 15.8 kg) completed ten repetitions at 50% of one-repetition maximum (1RM), and six repetitions at 75% 1RM for both BP and SQ. Four PUSH devices were utilized and attached to the subject’s right forearm, the center barbell, left and right sides of the barbell. MC markers were placed on top of each PUSH device. An overall analysis using a series of least-squares means contrasts suggested CMV did not differ (p > 0.05) between measurement technologies when position, RE, intensity and repetitions were combined. PUSH exhibited the highest Intraclass Correlation Coefficients (ICC = 0.835−0.961) and Pearson Product-Moment Correlation Coefficients (r = 0.742−0.949) at the arm and center barbell locations when compared with MC. The measurement of CMV between MC and PUSH compares favorably during moderate (i.e., 50%) and high (75%) intensity SQ and BP RE. These data indicate individuals can use the PUSH band v2.0 to accurately monitor CMV within a RE set for SQ and BP RE.

Validation of an Automatic Inertial Sensor-Based Methodology for Detailed Barbell Velocity Monitoring during Maximal Paralympic Bench Press

Current technologies based on inertial measurement units (IMUs) are considered valid and reliable tools for monitoring barbell velocity in strength training. However, the extracted outcomes are often limited to a few velocity metrics, such as mean or maximal velocity. This study aimed at validating a single IMU-based methodology to automatically obtain the barbell velocity full profile as well as key performance metrics during maximal Paralympic bench press. Seven Paralympic powerlifters (age: 30.5 ± 4.3 years, sitting height: 71.6 ± 6.8 cm, body mass: 72.5 ± 16.4 kg, one-repetition maximum: 148.4 ± 38.6 kg) performed four attempts of maximal Paralympic bench press. The barbell velocity profile and relevant metrics were automatically obtained from IMU linear acceleration through a custom-made algorithm and validated against a video-based reference system. The mean difference between devices was 0.00 ± 0.04 m·s−1 with low limits of agreement (<0.09 m·s−1) and moderate-to-good reliability (ICC: 0.55−0.90). Linear regression analysis showed large-to-very large associations between paired measurements (r: 0.57−0.91, p < 0.003; SEE: 0.02−0.06 m·s−1). The analysis of velocity curves showed a high spatial similarity and small differences between devices. The proposed methodology provided a good level of agreement, making it suitable for different applications in barbell velocity monitoring during maximal Paralympic bench press.

Validity and Reliability of the Leomo Motion-Tracking Device Based on Inertial Measurement Unit with an Optoelectronic Camera System for Cycling Pedaling Evaluation

Background: The use of inertial measurement sensors (IMUs), in the search for a more ecological measure, is spreading among sports professionals with the aim of improving the sports performance of cyclists. The kinematic evaluation using the Leomo system (TYPE-R, Leomo, Boulder, CO, USA) has become popular. Purpose: The present study aimed to evaluate the reliability and validity of the Leomo system by measuring the angular kinematics of the lower extremities in the sagittal plane during pedaling at different intensities compared to a gold-standard motion capture camera system (OptiTrack, Natural Point, Inc., Corvallis, OR, USA). Methods: Twenty-four elite cyclists recruited from national and international cycling teams performed two 6-min cycles of cycling on a cycle ergometer at two different intensities (first ventilatory threshold (VT1) and second ventilatory threshold (VT2)) in random order, with a 5 min rest between intensity conditions. The reliability and validity of the Leomo system versus the motion capture system were evaluated. Results: Both systems showed high validity and were consistently excellent in foot angular range Q1 (FAR (Q1)) and foot angular range (FAR) (ICC-VT1 between 0.91 and 0.95 and ICC-VT2 between 0.88 and 0.97), while the variables leg angular range (LAR) and pelvic angle showed a modest validity (ICC-VT1 from 0.52 to 0.71 and ICC-VT2 between 0.61 and 0.67). Compared with Optitrack, Leomo overestimated all the variables, especially the LAR and pelvic angle values, in a range between 12 and 15°. Conclusions: Leomo is a reliable and valid tool for analyzing the ranges of motion of the cyclist’s lower limbs in the sagittal plane, especially for the variables FAR (Q1) and FAR. However, its systematic error for FAR and Pelvic Angle values must be considered in sports performance analysis.

Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set.

Validity of Dual-Minima Algorithm for Heel-Strike and Toe-Off Prediction for the Amputee Population

Assessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard, especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. The results showed considerable accuracy for the HS with a median error of −1 ms. The TO prediction error was larger with the median ranging from 35–84 ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects’ mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and the amputee’s functional ability level.

Comparison of Velocity and Percentage-based Training on Maximal Strength：Meta-Analysis

The purpose was to analyze the comparison of velocity-based resistance training and one-repetition maximum (%1RM) percentage-based training in maximal strength improvement by meta-analyzing and to find the reasons for the controversial findings of different studies. Ten studies were included in the systematic review and seven were subjected to meta-analysis. A total of 139 subjects were selected from the included articles after exclusion, including athletes of different specialties (N=93) and non-athletes mainly from fitness groups (N=46). The overall effect size was SMD=0.26 (95%CL 0.03 to 0.49, P=0.03, I²=0). As for the comparison of the analysis of different intervention objects as subgroups, the effect size of athletes as the subgroup was 0.35 (95%CI 0.06 to 0.64, p=0.02, I²=0), indicating that in the RCT with athletes as the intervention target, the effect of VBRT in improving the maximal strength was significantly different from that of PBT. Velocity-based resistance training might be more effective than percentage-based training in maximal strength improvement, in which velocity-based resistance training is more suitable for athletes in season, while percentage-based training is more suitable for the general sports population. More high-quality researches should deal with the effect of other athletic performance with velocity-based resistance training in the future.

Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.

Impact of Training Protocols on Lifting Velocity Recovery in Resistance Trained Males and Females

It has been suggested that sex differences exist in recovery following strength training. This study aimed to investigate the differences in recovery kinetics between resistance trained males and females following two different back squat (BSq) protocols. The first protocol (eight females and eight males) consisted of five sets of five repetitions at 80% of their one-repetition maximum (1RM) in the BSq (SMRT), while the second (seven females and eight males) consisted of five sets to muscular failure (MF) with a 4–6RM load (RMRT). The recovery was quantified with the mean concentric velocity (MV) at 80% of the 1RM immediately before and 5 min, 24, 48, and 72 h after the training protocol. Following the SMRT, a significant between-sex difference, favoring the females, was observed at 5 min, 24 h, and 48 h following the SMRT (p < 0.05, Effect Size (ES) = 1.01–2.25). Following the RMRT, only the males experienced a significant drop in performance after 5 min compared to the baseline (p = 0.025, ES = 1.34). However, no sex differences were observed at any timepoint (p > 0.05). These results suggest that males experienced more fatigue than females following a protocol where the volume relative to the 1RM was matched, while no differences in fatigue were evident following a protocol in which multiple sets were performed to MF.

Valid and Reliable Barbell Velocity Estimation Using an Inertial Measurement Unit

The accurate assessment of the mean concentric barbell velocity (MCV) and its displacement are crucial aspects of resistance training. Therefore, the validity and reliability indicators of an easy-to-use inertial measurement unit (VmaxPro^®) were examined. Nineteen trained males (23.1 ± 3.2 years, 1.78 ± 0.08 m, 75.8 ± 9.8 kg; Squat 1-Repetition maximum (1RM): 114.8 ± 24.5 kg) performed squats and hip thrusts (3–5 sets, 30 repetitions total, 75% 1RM) on two separate days. The MCV and displacement were simultaneously measured using VmaxPro^® and a linear position transducer (Speed4Lift^®). Good to excellent intraclass correlation coefficients (0.91 < ICC < 0.96) with a small systematic bias (p < 0.001; η_p² < 0.50) for squats (0.01 ± 0.04 m·s⁻¹) and hip thrusts (0.01 ± 0.05 m·s⁻¹) and a low limit of agreement (LoA < 0.12 m·s⁻¹) indicated an acceptable validity. The within- and between-day reliability of the MCV revealed good ICCs (0.55 < ICC < 0.91) and a low LoA (<0.16 m·s⁻¹). Although the displacement revealed a systematic bias during squats (p < 0.001; η_p² < 0.10; 3.4 ± 3.4 cm), no bias was detectable during hip thrusts (p = 0.784; η_p² < 0.001; 0.3 ± 3.3 cm). The displacement showed moderate to good ICCs (0.43 to 0.95) but a high LoA (7.8 to 10.7 cm) for the validity and (within- and between-day) reliability of squats and hip thrusts. The VmaxPro^® is considered to be a valid and reliable tool for the MCV assessment.

Validity and Effects of Placement of Velocity-Based Training Devices

Velocity-based training (VBT) is a resistance training method by which training variables are manipulated based on kinematic outcomes, e.g., barbell velocity. The better precision for monitoring and manipulating training variables ascribed to VBT assumes that velocity is measured and communicated correctly. This study assessed the validity of several mobile and one stationary VBT device for measuring mean and peak concentric barbell velocity over a range of velocities and exercises, including low- and high-velocity, ballistic and non-ballistic, and plyometric and non-plyometric movements, and to quantify the isolated effect of device attachment point on measurement validity. GymAware (r = 0.90-1, standard error of the estimate, SEE = 0.01-0.08 m/s) and Quantum (r = 0.88-1, SEE = 0.01-0.18 m/s) were most valid for mean and peak velocity, with Vmaxpro (r = 0.92-0.99, SEE = 0.02-0.13 m/s) close behind. Push (r = 0.69-0.96, SEE = 0.03-0.17 m/s) and Flex (r = 0.60-0.94, SEE = 0.02-0.19 m/s) showed poorer validity (especially for higher-velocity exercises), although typical errors for mean velocity in exercises other than hang power snatch were acceptable. Effects of device placement were detectable, yet likely small enough (SEE < 0.1 m/s) to be negligible in training settings.

Classification of Tennis Shots with a Neural Network Approach

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F₁ score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete’s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomarkers and/or other performance indicators that are crucial to evaluate the evolution of the athlete’s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete’s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more—and more effective—biomarkers regarding the athlete’s internal workload, which limits its wider adoption by the sports community.