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.

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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-min running-based football match simulation. Before and after every 15 min 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.

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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.@en

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The disadvantage of these measurement systems is that they do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. Rapid development in sensor technology may overcome the limitations of the current measurement systems. With inertial measurement units (IMUs) securely fixed to body segments, sensor fusion algorithms and a biomechanical model, joint kinematics could be estimated. The main purpose of this article is to demonstrate a sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field. Five male subjects (age 22.5 ± 2.1 years; body mass 77.0 ± 3.8 kg; height 184.3 ± 5.2 cm; training experience 15.3 ± 4.8 years) performed a maximal 30-meter linear sprint. Hip and knee joint angles and angular velocities were obtained by five IMUs placed on the pelvis, both thighs and both shanks. Hip angles ranged from 195° (± 8°) extension to 100.5° (± 8°) flexion and knee angles ranged from 168.6° (± 12°) minimal flexion and 62.8° (± 12°) maximal flexion. Furthermore, hip angular velocity ranged between 802.6 °·s^-1 (± 192 °·s^-1) and-674.9 °·s^-1 (± 130 °·s^-1). Knee angular velocity ranged between 1155.9 °·s^-1 (± 200 °·s^-1) and-1208.2 °·s^-1 (± 264 °·s^-1). The sensor setup has been validated and could provide additional information with regard to athlete monitoring in the field. This may help professionals in a daily sports setting to evaluate their training programs, aiming to reduce injury and optimize performance.

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INTRODUCTION: It has been known that effects of Soft Tissue Artefact (STA) cause inaccuracy of motion tracking when using skin-attached markers for kinematic studies (1) (2). However no study has been found to compare the displacement of different marker locations for the implementation of IMUs in soccer clothing. In this study, the displacements of seven marker locations were compared to segments of a Plugin Gait system (3) during acceleration and deceleration trials. The seven marker locations were determined by a preliminary study on comfort and risk of different marker locations during soccer-specific movements, resulting in a ranking of the marker locations. METHODS: Four able bodied subjects (2M 2F) performed five repetitions of both acceleration (ACC; 0% - 100%) and deceleration movements (DEC; 100% - 0%). Optoelectronic measurements were done in an indoor space surrounded by eight optoelectronic VICON cameras (100 FPS). Sixteen markers placed on bony landmarks were used to model joint centre coordinates of the hip, knee and ankle to simulate bone segments of the thigh and shank to serve as a reference frame. C3D data of markers was processed in Matlab® to calculate displacement both perpendicular and parallel to the reference frame. Parallel displacement is measured relatively and measured from the knee joint centre due to non-constant segment length caused by the modelled hip joint centre. RESULTS: The total number of data points after eliminating outliers is 963 (488 perp. and 475 para.). Each data point represents the maximum shift for each marker per trial for both legs as the range of distances between the marker and the reference frame. The markers with significant lowest mean displacement according to an ANOVA parametric test (with Post Hoc Scheffe, p = 0.05) are the ones located on the lateral side of the shank and thigh (both 2/3 distal). Significant higher displacement occurs on the Semimembranosus, Vastus Lateralis and Gastrocnemius. Markers located on the lower hamstring (7 cm proximal to knee joint centre, posterior) and Popliteus are neither in the significantly lowest or highest subset of markers. CONCLUSION: Optoelectronic markers on the lateral side of the thigh and shank cause lower amount of perpendicular and parallel errors in kinematic measurements compared to five other marker locations. As the lateral shank was also recommended in the preliminary study, it would be a good marker location to both minimize comfort issues and measurement errors caused by STA. Lateral thigh locations, however, were perceived to cause discomfort. The designer should consider using the marker location on the Popliteus to balance between lowest displacement and comfort. It should be noted that displacement highly depends on type of movement and its vector, of which parallel displacement values could be affected by systematic errors of the Plugin Gait model. @en