9 research outputs found
WHY IS THE LEFT KNEE RATHER PRONE TO INJURY DURING TEAM HANDBALL-SPECIFIC SIDE-CUTTING MANEUVERS TO THE RIGHT?
The purpose of this study was the biomechanical inter-leg evaluation in three team handball-specific side-cutting maneuvers. This should help to gain a better understanding how different movement executions potentially produce harmful demands to one or both knee joints. Therefore, eight competitive handball player performed the three most common side-cutting maneuvers to the right side (side-cutting maneuver was performed with alternating or simultaneous steps) in a game-like setting in a movement laboratory. Movement data were collected with a 3D motion capture system and two linked 3D force plates. The analysis of the side-cutting maneuvers revealed increased vertical and medio-lateral ground reaction force components on the left leg, which initiated the side-cutting maneuver. The peak knee abduction moments in the weight acceptance phase did not differ between the left and the right leg in all three side-cutting maneuvers. However, higher peak knee valgus angles occurred at the left leg, which increased with increasing stance time. The results of this study indicate that during the performance of handball-specific side-cutting maneuvers to the right, the left knee joint has a greater risk to get injured. Consequently, athletes and coaches should place special focus on the movement execution of the cutting initiating leg to reduce the risk of knee injuries. Furthermore, leg explosive strength and core stability should be in major focus in training exercises to prepare the athlete for the demands in such high intensity movements
A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks
IDENTIFICATION OF FATIGUE-RELATED KINEMATIC CHANGES IN ELITE RUNNERS USING A SUPPORT VECTOR MACHINE APPROACH
Understanding the kinematic changes underlying fatigue is essential in running biomechanics. The aim of this study was to identify fatigue-related kinematic changes in elite runners using a support vector machine approach. Full-body kinematics of thirteen trained runners were recorded in a non-fatigued and a fatigued state during treadmill running at their individual fatigue-speed. A support vector machine was trained and used to identify kinematic differences between the non-fatigued and fatigued state based on principal component scores. Strides during non-fatigued and fatigued running could be separated with 99.4% classification accuracy. Four upper limb (two shoulder and two elbow), four lower limb (one ankle, two knee and one hip) and two trunk (one thoracic and one lumbar spine) principal component scores were identified as most discriminative kinematic features between non-fatigued and fatigued running. The findings of the study suggest the feasibility of a support vector machine approach to identify subtle fatigue-related kinematic changes in elite runners
Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm
Rollator usage lets young individuals switch movement strategies in sit-to-stand and stand-to-sit tasks
Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications