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The gold standard for the analysis of human kinematics and kinetics is a camera-based motion capture system in combination with force measurement platforms. Alternatively, inertial measurement units can be utilized to obtain human kinematics, while ground reaction forces are computed from full body dynamics. This setup represents a system independent from the spatial confinement of a gait laboratory. The aim of this study is the comparison of the two methods by the investigation of lower limb kinematics and the resulting joint reaction forces within the ankle-, knee- and hip joints. For this purpose, human motion during gait was captured simultaneously by both measurement techniques. 13 trials from 8 different test subjects were evaluated in total. IMU data was processed with a quaternion based Kalman Filter. The data sets were implemented into a musculoskeletal simulation program in order to drive a virtual human body model. Each sensor was aligned to the gravitational and magnetic field vectors of the earth. The angles of flexions, extensions and rotations were analyzed to determine kinematic differences. Joint reaction forces defined kinetic dissimilarities. The overall kinematic differences of both models yielded root mean square errors of 7.62°, 6.02°, 4.95°, 2.79°, 2.38° and 3.56° for ankle flexion, subtalar eversion, knee flexion, hip external rotation, hip abduction and hip flexion, respectively. The proximo-distal differences in force peaks between the models yielded overall for the ankle, 57.33 %Bodyweight(BW) ± 46.86 %BW (16.66 %(Maximum peak to peak) ± 13.62 %) for the knee 37.09 %BW ± 29.33 %BW (17.65 % ± 15.44 %) and 32.03 %BW ± 24.33 %BW (15.6 % ± 12.54 %) for the hip. The overall outcome of this work investigated an approach independent of the common setup of the gait laboratory, thus enabling a cheaper and more flexible technology as an alternative. However, kinematic and thus kinetic differences remain rather large. Future work aims to improve the contact criterion for the calculation of the ground reaction forces and the implementation of a full-body calibration algorithm for the IMU system in order to counteract magnetic field disturbances.
Mental stress reduces performance and changes musculoskeletal loading in football-related movements
(2020)
Purpose:
Football players have a high risk of leg muscle injuries, especially when exposed to mental stress. Hence, this study investigated the musculoskeletal response of elite youth football players during highly dynamic movements under stress. The hypothesis is that mental stress reduces performance and changes the muscular forces exerted.
Materials & methods:
Twelve elite youth football players were subjected to mental stress while performing sports-specific change-of-direction movements. A modified version of the d2 attention test was used as stressor. The kinetics are computed using inverse dynamics. Running times and exerted forces of injury-prone muscles were analysed.
Results:
The stressor runs were rated more mentally demanding by the players (p = 0.006, rs = 0.37) with unchanged physical demand (p = 0.777, rs = 0.45). This resulted in 10% longer running times under stress (p < 0.001, d = −1.62). The musculoskeletal analysis revealed higher peak muscle forces under mental stress for some players but not for others.
Discussion:
The study shows that motion capture combined with musculoskeletal computation is suitable to analyse the effects of stress on athletes in highly dynamic movements. For the first time in football medicine, our data quantifies an association between mental stress with reduced football players’ performance and changes in muscle force.
BACKGROUND: Validation and verification of multibody musculoskeletal models sEMG is a difficult process because of the reliability of sEMG data and the complex relationship of muscle force and sEMG. OBJECTIVE: This work aims at comparing experimentally recorded and simulated muscle activities considering a numerical model for crosstalk. METHODS: For providing an experimentally derived reference data set, subjects were performing elevations of the arm, where the activities of the contemplated muscle groups were measured by sEMG sensors. Computed muscle activities were further processed and transformed into an artificial electromyographical signal, which includes a numerical crosstalk model. In order to determine whether the crosstalk model provides a better agreement with the measured muscle activities, the Pearson correlation coefficient has been computed as a qualitative way of assessing the curve progression of the data sets. RESULTS: The results show an improvement in the correlation coefficient between the experimental data and the simulated muscle activities when taking crosstalk into account. CONCLUSIONS: Although the correlation coefficient increased when the crosstalk model was utilized, it is questionable if the discretization of both, the crosstalk and the musculoskeletal model, is accurate enough.
Musculoskeletal modelling of the shoulder – effects on muscle recruitment and joint reaction force
(2019)
Football players have a high risk of leg muscle injuries, especially when exposed to mental stress. Injuries to muscles of the thigh are common in amateur and professional football, representing almost a third of all injuries. These injuries occur primarily in non-contact situations and from overuse. They can lead to a range of costs, including financial costs associated with treatment as well as those associated with long-term recovery, and absence from training and/or competition. Further, there is a high risk of injury recurrence and subsequent injury.