The analysis of lower body kinematics of a football player throughout a training or match can become a useful tool to improve currently used physical load estimates. Presently, radio frequency-based local positioning measurement systems (LPM) and satellite-based global positioning systems (GPS), which measure a player’s position on the field continuously, are widely used to quantify physical load during practice and competition [1
]. However, many explosive actions associated with high muscle loads, such as accelerations, decelerations, kicking, jumping, and side-cutting, do not necessarily involve large or fast global displacements [4
]. Even LPM, which is considered to be more accurate compared to GPS-based systems, does not provide accurate estimations of instantaneous acceleration during explosive movements [7
], while such movements place extensive mechanical loads on muscles, tendons, and joints. This is highly relevant because mechanical muscle loading is thought to be an important cause of muscle injuries in football, especially for muscles around the hip [3
]. Consequently, a considerable amount of external training load may be missed using LPM or GPS systems only. Therefore, external training load estimates in football may be improved by inclusion of lower body kinematics, which also opens the possibility for the quantification of football specific actions, like ball kicking, cutting movements, or explosive short distance sprints.
Movement kinematics have traditionally been obtained using optoelectronic motion analysis systems. However, these systems are restricted to a laboratory setting, have a limited measurement volume, and involve extensive start-up procedures, which make them unsuitable for use in daily football practice. Moreover, optoelectronic motion analysis requires a clear line-of-sight between cameras and markers, which may be obstructed by the ball or other players. Wearable inertial-based motion analysis systems have recently gained popularity because these systems allow for registration of movement kinematics without these limitations [9
]. Inertial-based motion analysis systems consist of inertial magnetic measurement units (IMUs) attached to body segments. These sensors directly measure linear acceleration, angular velocity, and magnetic field strength in three orthogonal axes. The orientation of each IMU is obtained by combining these sensor readings in sensor fusion algorithms [11
]. In combination with a biomechanical model, joint and body segment kinematics can be obtained [12
]. A sensor-to-segment calibration needs to be performed to construct the biomechanical model. A variety of methods have been used to do so. However, most of these methods require external devices or take up a considerable amount of time [13
]. To use an inertial-based motion analysis system to quantify lower body kinematics on a daily basis, sensor-to-segment calibration should be quick and easy to perform.
Inertial-based motion analysis systems have been applied successfully to estimate movement kinematics in sports such as marathon running [14
] and swimming [15
]. Such systems have also shown good agreement with gold standard optoelectronic systems in quantifying lower body kinematics during various football related activities studied in isolation, such as walking [17
], running [13
], and kicking [19
]. However, the accuracy of inertial motion analysis systems depends on the type of movement and the intensity at which a movement is performed [10
]. Moreover, soft tissue artefacts can be expected to increase in higher intensity movements. To the best of our knowledge, no studies have assessed the validity of an inertial based motion analysis system for a variety of common football specific movements, such as accelerating, decelerating, cutting movements, and turning. Moreover, the maximum running speed for which an inertial-based system has been validated is ~3.9 m/s [21
], while running speeds above 5.5 m/s frequently occur during professional football matches [22
]. Therefore, the validation of an inertial motion analysis system for football should include a wide variety of football-specific movements performed at high intensities.
The intensity of a movement may be estimated by measuring the angular velocity of the joints involved in a similar way to how exercise intensity is now determined from velocity measures obtained by LPM and GPS [1
]. In sprinting, for example, joint angular patterns are shown to be relatively invariable across a range of speeds. As a consequence, the energy associated with the lower limbs is approximately proportional to the joint angular velocities of legs [23
]. Therefore, movement intensities may be estimated by measuring joint angular velocities.
Considering the potential additional value of quantifying lower body kinematics to physical load estimates in daily football practice, this paper proposes a relatively simple inertial-based system. Especially, the sensor-to-segment calibration procedure is straightforward, fast, and does not require an experienced operator. The primary aim of the study was to assess the concurrent validity of the inertial-based system with an optoelectronic motion analysis system for a variety of football specific movements performed at submaximal and maximal intensities. Knee and hip flexion/extension angles and angular velocities were evaluated because most muscle injuries in football affect the muscles around the hip and knee [8
]. The secondary aim of the study was to establish whether movement intensities can be distinguished based on joint flexion/extension (FE) angular velocities obtained by the inertial-based system. We hypothesized that the inertial motion analysis system would show good concurrent validity with the optoelectronic system. However, we expected larger differences between the systems for movements performed at maximal intensities. Moreover, we anticipated that we would be able to differentiate between movement intensities based on joint angular (flexion/extension) velocities.
The repeated measures ANOVAs did not reveal any significant effects of body side on any of the analyzed variables (RMSD angles: p
= 0.128, η2
= 0.237, CMC angles; p
= 0.197 η2
= 0.160, RMSD angular velocities; p
= 0.540, η2
= 0.039, CMC angular velocities; p
= 0.824, η2
= 0.005, absolute angular velocity; p
= 0.618, η2
= 0.026). Therefore, we chose to only present the results of the left leg for clarity. Please refer to the Supplementary Materials
for the results of the right leg.
Examples of the joint angles and angular velocities of all movements performed at maximal intensity by one of the participants are shown in Figure 4
and Figure 5
, respectively. Mean RMSDs and CMC values of angles and angular velocities of all movements and movement intensities across the participants are shown in Table 1
and Table 2
, respectively. Furthermore, the mean absolute angular velocities for each movement and intensity are presented in Table 2
. There was a significant main effect of movement-type on RMSDs in joint angle (p
= 0.015, η2
= 0.347), as well as on joint angular velocity RMSDs (p
< 0.001, η2
= 0.960) and CMC’s (p
= 0.001, η2
= 0.939). Moreover, significant effects of joint on joint angle CMCs (p
< 0.001, η2
= 0.774), and on angular velocity RMSDs (p
< 0.001, η2
= 0.989) and CMCs (p
< 0.001, η2
= 0.779) were found. Higher CMCs were observed for the hip joint compared to the knee joint (p
< 0.001), while respective RMSDs were lower (p
< 0.001). Finally, movement intensity showed significant effects for RMSDs of joint angular velocities (p
< 0.001, η2
= 0.982), but not on angular velocity CMCs (p
= 0.077, η2
3.2. Running Tasks
Mean running speeds during the acceleration runs were 3.5 ± 0.5 m/s, 5.1 ± 0.6 m/s, and 6.6 ± 0.3 m/s for the low, medium, and maximal intensity trials, respectively. Across the running tasks and intensities, mean CMC values of knee angles were excellent and ranged 0.979 to 0.993, whereas corresponding mean RMSDs ranged from 4.4° to 6.4°. Mean CMC values of hip angles were between 0.854 and 0.985, with corresponding mean RMSDs between 6.5° and 10.9°.
3.3. Jumping and Kicking
In the jumping task, mean RMSDs in knee angles ranged 3.7° to 4.2°, whereas mean RMSDs in hip angles ranged from 6.7° to 7.6°. Over all intensities, mean CMC values were excellent for the knees (0.990–0.994) and ranged from very good to excellent for the hips (0.943–0.952). Mean RMSDs in joint angular velocities were between 104°/s and 133°/s for the knees, and between 54°/s and 63°/s for the hips. Moreover, CMC values for joint angular velocities ranged 0.878 to 0.894 and 0.932 to 0.945 for the knees and hips, respectively.
In the kicking tasks, mean RMSDs in joint angles were between 5.3° and 6.2° for the knees and between 7.4° and 8.3° for the hips. Mean corresponding CMC values were between 0.964 and 0.973 for the knees and between 0.957 and 0.971 for the hips, respectively. Mean CMC values for knee angular velocities ranged from 0.875 to 0.889 with mean RMSDs between 116°/s and 177°/s. For angular velocities of the hips, mean CMC values were ranged from 0.814 to 0.851, whereas mean RMSDs were between 78°/s and 121°/s.
3.4. Movement Intensity
Significant main effects of joint (p < 0.001, η2 = 0.975), intensity (p < 0.001, η2 = 0.935), and movement type (p < 0.001, η2 = 0.941) on absolute angular velocities were found. Absolute angular velocities were significantly higher for the knees compared to the hips (p < 0.001). All three movement intensities were significantly different from each other (p < 0.001), whereas absolute angular velocities were highest in the maximum intensity movements and lowest in the low intensity movements. All movements significantly differed from each other in terms of absolute angular velocity (p = 0.000–0.019), except the deceleration, which did not differ from the turn (p = 1.000) or the cut (p = 0.135).
Significant main effects of joint (p < 0.001, η2 = 0.767), intensity (p < 0.001, η2 = 0.659), and movement type (p = 0.021, η2 = 0.295) on absolute errors in absolute angular velocities were observed. However, when these errors were expressed as a percentage of the absolute angular velocities measured by the optoelectronic system, all main effects disappeared (joint: p = 0.708, η2 = 0.015, intensity: p = 0.600, η2 = 0.045), except for the effect of movement-type (p = 0.001, η2 = 0.552). Overall, relative errors of absolute angular velocity were 16.5% ± 1.3%.
The main aim of present study was to introduce and evaluate a simple inertial-based motion analysis system by assessing its concurrent validity with a gold standard optoelectronic motion analysis system during a variety of football specific movements performed at a range of intensities. The secondary aim was to establish whether different movement intensities could be distinguished based on joint flexion/extension angular velocities. The results showed very good to excellent correlations for knee angles over the range of movements and intensities performed, whereas the correlations for hip angles were good to excellent. Good to excellent correlations were found for knee and hip angular velocities. Moreover, mean absolute angular velocities clearly differed between low, medium, and maximal movement intensities.
RMSDs in knee angles (4–6°) during running were slightly larger than what has previously been found with inertial based motion analysis during walking [13
] and running [13
] (knee angle: RMSD < 3.4°). However, all these studies used marker data obtained by optoelectronic cameras to construct the biomechanical model of the inertial sensor system. Clearly, this is not possible when the inertial based motion analysis system is used as stand alone. Consequently, the results of these previous studies do not translate directly to on-field use of inertial-based motion analysis systems. In the independent use of inertial-based motion analysis systems, the biomechanical model is generally constructed based on pre-known postures, functional calibration procedures, or, as in our study, on a combination of both, rather than on the position of anatomical bony landmarks obtained with an additional system [19
]. This inevitably leads to differences between the biomechanical models of the inertial sensor and the optoelectronic system, which are dependent on marker placement for the optoelectronic system and on the execution of the calibration movements for the inertial based system. This effect probably contributed to the somewhat larger RMSDs in joint angles in comparison to these earlier studies [13
]. RMSDs in joint angular velocities were not previously reported but are likely to have been partially determined by this same effect. Although RMSDs in joint angular velocities appear to be relatively high (Table 2
), good to excellent correlations indicate that the course of angular velocity signals was similar between the optoelectronic motion analysis system and inertial based system.
The RMSDs and CMCs of joint angles found in the present study across a range of movements and intensities are comparable to results of walking, running, and kicking found in other studies that used independent biomechanical models [19
]. However, the maximum running speed that has been reported in these studies was ~3.9m/s [21
], whereas, in our study, participants had a mean running speeds of 3.5 ± 0.5 m/s, 5.1 ± 0.6 m/s, and 6.6 ± 0.3 m/s during the acceleration runs at low, medium, and maximal intensity, respectively. This indicates that our inertial-based motion analysis system still provides valid measures of joint angles at movements intensities that are considerably higher compared to previous research.
However, we expected that a relatively high movement intensity would affect the inertial-based motion system’s accuracy in the two following ways. First, the accuracy of an inertial-based system relies upon the performance of the orientation estimation of each individual sensor. One of the assumptions of sensor fusion algorithms is that the measured direction of the acceleration is equal to the direction of the gravitational acceleration [11
]. Therefore, the performance of orientation estimation is negatively influenced in presence of linear accelerations. As a result, the accuracy of inertial-based system may be lower during high intensity movements. Second, the presence of soft tissue between the bones and sensors on the skin can lead to soft tissue artefacts in sensor-derived segment orientations. Any deformation in soft tissue between a sensor and bone leads to errors in the estimated segment orientation. High-impact forces and strong muscle contractions associated with high-intensity movements may mean larger soft tissue deformations, which may result in a lower inertial-based system accuracy [35
]. Unexpectedly, we did not find an effect of movement intensity on CMC or RMSD in joint angles. An explanation could be that our low intensity movements were performed at about 50% of maximal intensity, which may have already been high enough to introduce substantial soft tissue deformations. As a consequence, differences in soft tissue artefacts between the movement intensities may have been too small to result in significant effects of movement intensity on the validity measures.
To the best of our knowledge, no previous studies have reported statistics on the similarity between complete joint angular velocity signals obtained by optoelectronic and inertial-based motion analysis systems. We found lower CMCs of joint angular velocities compared to angles, indicating a lower accuracy of the inertial-based motion analysis system in determining angular velocities compared to angles. Movement intensity had a significant effect on RMSDs in angular velocities and on absolute errors in absolute angular velocities. However, there were no main effects of movement intensity on the corresponding CMCs and relative errors in absolute angular velocities. These results suggest that the absolute errors in angular velocity measurements are proportional to the magnitude of joint angular velocity. This proportionality, as well as the error margins, should be considered when interpreting joint angular velocities in daily football practice situations.
Movement intensity measures are frequently used in football practice to estimate training load [1
]. However, previously available methods to measure movement intensity are unable to estimate intensity of movements with small global displacements that may still be accompanied by the high mechanical loading of ligaments, tendons, and muscles, such as kicking, jumping, and short sprints. The significant effect of movement intensity on absolute angular velocities in the present study shows that the intensity of all six investigated movements, including kicking and jumping, can be estimated by measuring joint angular velocities. Yet, it should be noted that the type of movement also largely determined the presented joint angular velocities. Therefore, the effects of movement intensity on angular velocities cannot be directly compared among different movements. Consequently, automatic movement recognition algorithms may have to be included in the estimations of training load.
The sensor setup used in present study does not allow football players to make slide tackles because the sensors may come off and/or bruise the player. In addition, equipping many individual players with five separate sensors is not feasible in daily practice. Therefore, we are currently working on integration of the sensors into tights or shorts, which have a centralized power supply placed at the lower back where it has less impact with the ground during slide tackles. This also makes it possible to further miniaturize the individual sensor units, since the battery is, by far, the largest component of the units used in the present study.