Inertial Sensor-Based Motion Tracking in Football with Movement Intensity Quantification

A Novel Method and System Implementation for Precise Estimation of Single-Axis Rotational Angles

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis-angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range.

Validity of Inertial Measurement Unit (IMU Sensor) for Measurement of Cervical Spine Motion, Compared with Eight Optoelectronic 3D Cameras Under Spinal Immobilization Devices

Background

The assessment of cervical spine motion is critical for out-of-hospital patients who suffer traumatic spinal cord injuries, given the profound implications such injuries have on individual well-being and broader public health concerns. 3D Optoelectronic systems (BTS SmartDX) are standard devices for motion measurement, but their price, complexity, and size prevent them from being used outside of designated laboratories. This study was designed to evaluate the accuracy and reliability of an inertial measurement unit (IMU) in gauging cervical spine motion among healthy volunteers, using a 3D optoelectronic motion capture system as a reference.

Methods

Twelve healthy volunteers participated in the study. They underwent lifting, transferring, and tilting simulations using a long spinal board, a Sked stretcher, and a vacuum mattress. During these simulations, cervical spine angular movements-including flexion-extension, axial rotation, and lateral flexion-were concurrently measured using the IMU and an optoelectronic device. We employed the Wilcoxon signed-rank test and the Bland-Altman plot to assess reliability and validity.

Results

A single statistically significant difference was observed between the two devices in the flexion-extension plane. The mean differences across all angular planes ranged from -1.129° to 1.053°, with the most pronounced difference noted in the lateral flexion plane. Ninety-five percent of the angular motion disparities ascertained by the SmartDX and IMU were less than 7.873° for the lateral flexion plane, 11.143° for the flexion-extension plane, and 25.382° for the axial rotation plane.

Conclusion

The IMU device exhibited robust validity when assessing the angular motion of the cervical spine in the axial rotation plane and demonstrated commendable validity in both the lateral flexion and flexion-extension planes.

Different Aspects of Physical Load in Small-Sided Field Hockey Games

ABSTRACT

Wilmes, E, de Ruiter, CJ, van Leeuwen, RR, Banning, LF, van der Laan, D, and Savelsbergh, GJP. Different aspects of physical load in small-sided field hockey games. J Strength Cond Res 38(2): e56-e61, 2024-Running volumes and acceleration/deceleration load are known to vary with different formats of small-sided games (SSGs) in field hockey. However, little is known about other aspects of the physical load. Therefore, the aim of this study was to gain a more thorough understanding of the total physical load in field hockey SSGs. To that end, 2 different SSGs (small: 5 vs. 5, ∼100 m 2 per player; large: 9 vs. 9, ∼200 m 2 per player) were performed by 16 female elite field hockey athletes. A range of external physical load metrics was obtained using a global navigational satellite system and 3 wearable inertial measurement units on the thighs and pelvis. These metrics included distances covered in different velocity ranges (walk, jog, run, and sprint), mean absolute acceleration/deceleration, Hip Load, and time spent in several physically demanding body postures. The effects of SSG format on these external physical load metrics were assessed using linear mixed models ( p < 0.05). Running volumes in various speed ranges were higher for the large SSG. By contrast, mean absolute acceleration/deceleration and time spent in several demanding body postures were higher for the small SSG. This study shows that changing the SSG format affects different aspects of physical load differently.

Discriminative validity of summarized hip and knee angular accelerations for lower extremity training load quantification in male soccer players during a standardised training drill

This study assessed the discriminative validity of summarized hip and knee angular accelerations during a standardized training drill. Twenty-eight soccer players performed a standardized training drill that mimics game demands. Discriminative validity was examined by assessment of between-group differences of summarized preferred kicking leg hip and knee angular accelerations, and Playerload between national and regional soccer players for the full training drill, and parts based on locomotor intensity, or additional pass and jumping header activities. Furthermore, relationships were assessed between the summarized hip and knee angular accelerations and conventional load indicators derived from a local positioning measurement system, such as high-intensity running distance and Playerload. National players had higher summarized hip (Mean difference: 62.7 A.U. ES = 0.77, p = 0.049) and knee (Mean difference: 137.1 A.U. ES = 1.06, p = 0.008) angular accelerations. Significant interaction effects were observed during high-intensity running (Hip: 0.2 A.U./m, ES = 0.98, p = 0.005; Knee: 0.61 A.U./m, ES = 1.52, p < 0.001), and sprinting (Hip: 0.3 A.U./m, ES = 1.01, p < 0.02; Knee: 0.56 A.U./m, ES = 1.57, p < 0.001). Between-group differences were not present for additional passing or jumping header activities. Compared to summarized hip and knee angular accelerations, Playerload had less ability to discriminate between players and activities. Moreover, the lower extremity training load indicators were unrelated to conventional load indicators. Together these results confirm discriminative validity of summarized hip and knee angular acceleration training load indicators during a standardised training drill.

Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland-Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland-Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and -3.3° to -4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate.

New training load metrics in field hockey using inertial measurement units

Field hockey players are exposed to high biomechanical loads. These loads often cannot be adequately estimated with global navigational satellite systems (GNSS) since on-field displacements during these movements are often small. Therefore, this study aims to explore the potential of different proxies of biomechanical load in field hockey with use of a simple inertial measurement unit (IMU) system. Sixteen field hockey players performed a range of field hockey specific exercises, including running with stick on the ground, running upright, and different types of shots and passes. All exercises were performed at two different frequencies (i.e. number of actions per minute). A variety of proxies of biomechanical load (time spent with forward tilted pelvis, time spent in lunge position, time spent with flexed thighs, and Hip Load) were obtained using wearable IMUs. In addition, total distance was quantified using a GNSS system. Linear mixed models were constructed to determine the effects of the different exercises and action frequency on all quantified metrics. All metrics increased approximately proportional to the increase in action frequency. Total distance and Hip Load were greatest for the running exercises, but the different types of shots and passes had greater effects on specific on the times spent in the demanding body postures. This shows that these proxies of biomechanical load can be used to estimate field hockey-specific biomechanical loads. The use of these metrics may provide coaches and medical staff with a more complete view of the training load that field hockey players experience.Highlights New proxies of biomechanical load derived with inertial measurement units were used to quantify field hockey specific biomechanical loads.These new biomechanical metrics are complementary to metrics obtained with global navigation satellite systems and increased proportionally to a doubling of the exercise intensity.The presented biomechanical load metrics can help field hockey coaches to achieve a better balance between load and recovery for their players.

Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds

This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.

The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players

This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players' athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player's attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.

Construct Validity and Test-Retest Reliability of Hip Load Compared With Playerload During Football-Specific Running, Kicking, and Jumping Tasks

PURPOSE

To determine the test-retest reliability of the recently developed Hip Load metric, evaluate its construct validity, and assess the differences with Playerload during football-specific short-distance shuttle runs.

METHODS

Eleven amateur football players participated in 2 identical experimental sessions. Each session included 3 different shuttle runs that were performed at 2 pace-controlled running intensities. The runs consisted of only running, running combined with kicks, and running combined with jumps. Cumulative Playerload and Hip Loads of the preferred and nonpreferred kicking leg were collected for each shuttle run. Test-retest reliability was determined using intraclass correlations, coefficients of variation, and Bland-Altman analyses. To compare the load metrics with each other, they were normalized to their respective values obtained during a 54-m run at 9 km/h. Sensitivity of each load metric to running intensity, kicks, and jumps was assessed using separate linear mixed models.

RESULTS

Intraclass correlations were high for the Hip Loads of the preferred kicking leg (.91) and the nonpreferred kicking leg (.96) and moderate for the Playerload (.87). The effects (95% CIs) of intensity and kicks on the normalized Hip Load of the kicking leg (intensity: 0.95 to 1.50, kicks: 0.36 to 1.59) and nonkicking leg (intensity: 0.96 to 1.53, kicks: 0.06 to 1.34) were larger than on the normalized Playerload (intensity: 0.12 to 0.25, kicks: 0.22 to 0.53).

CONCLUSIONS

The inclusion of Hip Load in training load quantification may help sport practitioners to better balance load and recovery.

Quantification of Error Sources with Inertial Measurement Units in Sports

BACKGROUND

Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filter. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to present a method to quantify each of the sources of error of an IMU separately.

METHODS

An optoelectronic system was used as a gold standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different football-specific movements, varying both in the type of movement, and in movement intensity.

RESULTS

The error of the definition of the body frames (11.3-18.7 deg RMSD), the STA (3.8-9.1 deg RMSD) and the error of the orientation filter (3.0-12.7 deg RMSD) were all quantified separately for each body segment.

CONCLUSIONS

The error sources of IMU-based motion analysis were quantified separately. This allows future studies to quantify and optimize the effects of error reduction techniques.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

On-Field Biomechanical Assessment of High and Low Dive in Competitive 16-Year-Old Goalkeepers through Wearable Sensors and Principal Component Analysis

Diving saves are the main duty of football goalkeepers. Few biomechanical investigations of dive techniques have been conducted, none in a sport-specific environment. The present study investigated the characteristics of goalkeepers’ dive in preferred (PS) and non-preferred (nPS) side through an innovative wearables-plus-principal-component analysis (PCA) approach. Nineteen competitive academy goalkeepers (16.5 ± 3.0 years) performed a series of high and low dives on their PS and nPS. Dives were performed in a regular football goal on the pitch. Full-body kinematics were collected through 17 wearable inertial sensors (MTw Awinda, Xsens). PCA was conducted to reduce data dimensionality (input matrix 310,878 datapoints). PCA scores were extracted for each kinematic variable and compared between PS and nPS if their explained variability was >5%. In high dive, participants exhibited greater hip internal rotation and less trunk lateral tilt (p < 0.047, ES > 0.39) in PS than nPS. In low dives, players exhibited greater ipsilateral hip abduction dominance and lower trunk rotation (p < 0.037, ES > 0.40) in PS than nPS. When diving on their nPS, goalkeepers adopted sub-optimal patterns with less trunk coordination and limited explosiveness. An ecological testing through wearables and PCA might help coaches to inspect relevant diving characteristics and improve training effectiveness.

Motion Analysis of Football Kick Based on an IMU Sensor

A greater variety of technologies are being applied in sports and health with the advancement of technology, but most optoelectronic systems have strict environmental restrictions and are usually costly. To visualize and perform quantitative analysis on the football kick, we introduce a 3D motion analysis system based on a six-axis inertial measurement unit (IMU) to reconstruct the motion trajectory, in the meantime analyzing the velocity and the highest point of the foot during the backswing. We build a signal processing system in MATLAB and standardize the experimental process, allowing users to reconstruct the foot trajectory and obtain information about the motion within a short time. This paper presents a system that directly analyzes the instep kicking motion rather than recognizing different motions or obtaining biomechanical parameters. For the instep kicking motion of path length around 3.63 m, the root mean square error (RMSE) is about 0.07 m. The RMSE of the foot velocity is 0.034 m/s, which is around 0.45% of the maximum velocity. For the maximum velocity of the foot and the highest point of the backswing, the error is approximately 4% and 2.8%, respectively. With less complex hardware, our experimental results achieve excellent velocity accuracy.

A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System

This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.

Concurrent validity of an easy-to-use inertial measurement unit-system to evaluate sagittal plane segment kinematics during overground sprinting at different speeds

This study investigated concurrent validity of inertial measurement units (IMUs) and high-speed video for sagittal plane kinematics during overground sprinting. The practical relevance is demonstrated by reporting the changes in thigh kinematics in relation to toe-off and touch-down of the feet at near maximal to maximal (80-100%) speeds. Sixteen athletes ran multiple 60 m sprints with IMUs on their feet, shanks, thighs, pelvis and trunk. High-speed video data were captured of the start strides and of one complete stride at full speed. Coefficients of multiple correlation with video were >0.99 for angles and angular velocities of the thigh and shank but low for the pelvis and trunk (0.13-0.66). For the limb segment angles (minimum, maximum, at toe-off and at touch-down) absolute biases (limits of agreement) were ≤2.9°(≤7.7°) and for angular velocities the values were ≤57°.s-1(≤93°.s-1). Many of the expected speed-related changes in thigh kinematics were significant (linear mixed effect regression; p < 0.05).In conclusion, an easy-to-use IMU system has good concurrent validity with video, especially for the thigh. It registers the kinematics of all strides in multiple sprints and can detect relatively small changes thereof, including those at key moments of foot-touch-down and toe-off.

Biomechanical Load Quantification Using a Lower Extremity Inertial Sensor Setup During Football Specific Activities

Training load monitoring systems in football do not focus on lower extremities and therefore potentially neglect important information to optimise performance or reduce injury risk. The current study aims to present joint and segment angular accelerations as novel indicators to quantify lower extremity biomechanical load measured by a new inertial sensor setup. Relationships were explored with commonly used whole-body training load indicators using principal component analysis (PCA). Sixteen male amateur football players performed a linear sprint and an agility T-test. An inertial sensor setup, and local position measurement system were used to collect training load data. Hip Load, Knee Load, Thigh Load and Shank Load were introduced to quantify lower extremity biomechanical load. Three principal components were identified for both tests, explaining 91% and 86% of the variance. The indicators for the lower extremities contributed to the second principal component for both tests and provide distinct information compared to whole-body load indicators. The results show the potential to use an inertial sensor setup combined with common monitoring systems to evaluate training load, which may help optimise future performance and reduce injury risk. These relationships should be further examined during other football specific activities such as shooting or jumping.

An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks.

Flipping food during grilling tasks, a dataset of utensils kinematics and dynamics, food pose and subject gaze

This paper presents a multivariate dataset of 2866 food flipping movements, performed by 4 chefs and 5 home cooks, with different grilled food and two utensils (spatula and tweezers). The 3D trajectories of strategic points in the utensils were tracked using optoelectronic motion capture. The pinching force of the tweezers, the bending force and torsion torque of the spatula were also recorded, as well as videos and the subject gaze. These data were collected using a custom experimental setup that allowed the execution of flipping movements with freshly cooked food, without having the sensors near the dangerous cooking area. Complementary, the 2D position of food was computed from the videos. The action of flipping food is, indeed, gaining the attention of both researchers and manufacturers of foodservice technology. The reported dataset contains valuable measurements (1) to characterize and model flipping movements as performed by humans, (2) to develop bio-inspired methods to control a cooking robot, or (3) to study new algorithms for human actions recognition.

Stride Lengths during Maximal Linear Sprint Acceleration Obtained with Foot-Mounted Inertial Measurement Units

Inertial measurement units (IMUs) fixed to the lower limbs have been reported to provide accurate estimates of stride lengths (SLs) during walking. Due to technical challenges, validation of such estimates in running is generally limited to speeds (well) below 5 m·s⁻¹. However, athletes sprinting at (sub)maximal effort already surpass 5 m·s⁻¹ after a few strides. The present study aimed to develop and validate IMU-derived SLs during maximal linear overground sprints. Recreational athletes (n = 21) completed two sets of three 35 m sprints executed at 60, 80, and 100% of subjective effort, with an IMU on the instep of each shoe. Reference SLs from start to ~30 m were obtained with a series of video cameras. SLs from IMUs were obtained by double integration of horizontal acceleration with a zero-velocity update, corrected for acceleration artefacts at touch-down of the feet. Peak sprint speeds (mean ± SD) reached at the three levels of effort were 7.02 ± 0.80, 7.65 ± 0.77, and 8.42 ± 0.85 m·s⁻¹, respectively. Biases (±Limits of Agreement) of SLs obtained from all participants during sprints at 60, 80, and 100% effort were 0.01% (±6.33%), −0.75% (±6.39%), and −2.51% (±8.54%), respectively. In conclusion, in recreational athletes wearing IMUs tightly fixed to their shoes, stride length can be estimated with reasonable accuracy during maximal linear sprint acceleration.

Smart sensor tights: Movement tracking of the lower limbs in football

This article presents a novel smart sensor garment with integrated miniaturized inertial measurements units (IMUs) that can be used to monitor lower body kinematics during daily training activities, without the need of extensive technical assistance throughout the measurements. The smart sensor tights enclose five ultra-light sensor modules that measure linear accelerations, angular velocities, and the earth magnetic field in three directions. The modules are located at the pelvis, thighs, and shanks. The garment enables continuous measurement in the field at high sample rates (250 Hz) and the sensors have a large measurement range (32 g, 4,000°/s). They are read out by a central processing unit through an SPI bus, and connected to a centralized battery in the waistband. A fully functioning prototype was built to perform validation studies in a lab setting and in a field setting. In the lab validation study, the IMU data (converted to limb orientation data) were compared with the kinematic data of an optoelectronic measurement system and good validity (CMCs >0.8) was shown. In the field tests, participants experienced the tights as comfortable to wear and they did not feel restricted in their movements. These results show the potential of using the smart sensor tights on a regular base to derive lower limb kinematics in the field.

Associations between Hamstring Fatigue and Sprint Kinematics during a Simulated Football (Soccer) Match

PURPOSE

Neuromuscular fatigue is considered to be important in the etiology of hamstring strain injuries in football. Fatigue is assumed to lead to decreases in hamstring contractile strength and changes in sprinting kinematics, which would increase hamstring strain injury risk. Therefore, the aim was to examine the effects of football-specific fatigue on hamstring maximal voluntary torque (MVT) and rate of torque development (RTD), in relation to alterations in sprinting kinematics.

METHODS

Ten amateur football players executed a 90-minute running based football match simulation. Before and after every 15 minutes of simulated play MVT and RTD of the hamstrings were obtained in addition to the performance and lower body kinematics during a 20 m maximal sprint. Linear mixed models and repeated measurement correlations were used to assess changes over time and common within participant associations between hamstring contractile properties and peak knee extension during the final part of the swing phase, peak hip flexion, peak combined knee extension and hip flexion, and peak joint angular velocities, respectively.

RESULTS

Hamstring MVT and sprint performance were significantly reduced by 7.5% and 14.3% at the end of the football match simulation. Unexpectedly, there were no indications for reductions in RTD when MVT-decrease was considered. Decreases in hamstring MVT were significantly correlated to decreases in peak knee angle (R = 0.342) and to increases in the peak combined angle (R = -0.251).

CONCLUSIONS

During a football match simulation, maximal voluntary isometric hamstring torque declines. This decline is related to greater peak knee extension and peak combined angle during sprint running, which indicates a reduced capacity of the hamstrings to decelerate the lower leg during sprint running with fatigue.

Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and Kinematic Measures

Inertial-based motion capture (IMC) has been suggested to overcome many of the limitations of traditional motion capture systems. The validity of IMC is, however, suggested to be dependent on the methodologies used to process the raw data collected by the inertial device. The aim of this technical summary is to provide researchers and developers with a starting point from which to further develop the current IMC data processing methodologies used to estimate human spatiotemporal and kinematic measures. The main workflow pertaining to the estimation of spatiotemporal and kinematic measures was presented, and a general overview of previous methodologies used for each stage of data processing was provided. For the estimation of spatiotemporal measures, which includes stride length, stride rate, and stance/swing duration, measurement thresholding and zero-velocity update approaches were discussed as the most common methodologies used to estimate such measures. The methodologies used for the estimation of joint kinematics were found to be broad, with the combination of Kalman filtering or complimentary filtering and various sensor to segment alignment techniques including anatomical alignment, static calibration, and functional calibration methods identified as being most common. The effect of soft tissue artefacts, device placement, biomechanical modelling methods, and ferromagnetic interference within the environment, on the accuracy and validity of IMC, was also discussed. Where a range of methods have previously been used to estimate human spatiotemporal and kinematic measures, further development is required to reduce estimation errors, improve the validity of spatiotemporal and kinematic estimations, and standardize data processing practices. It is anticipated that this technical summary will reduce the time researchers and developers require to establish the fundamental methodological components of IMC prior to commencing further development of IMC methodologies, thus increasing the rate of development and utilisation of IMC.