50 research outputs found

    A Muscle Load Feedback Application for Strength Training:A Proof-of-Concept Study

    No full text
    Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into ‘control’, ‘partial feedback’, and ‘complete feedback’ groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (β = −18.9; 95% CI [−29.3, −8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload.Biomechatronics & Human-Machine Contro

    A Muscle Load Feedback Application for Strength Training: A Proof-of-Concept Study

    No full text
    Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into ‘control’, ‘partial feedback’, and ‘complete feedback’ groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (β = −18.9; 95% CI [−29.3, −8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload.Biomechatronics & Human-Machine Contro

    Magnitude and variability of individual elbow load in repetitive baseball pitching

    No full text
    In baseball pitchers the elbow is exposed to high and repetitive loads (i.e. external valgus torque), caused by pitching a high number of balls in a practice session or game. This can result in overuse injuries like the ulnar collateral ligament (UCL) injury. To understand injury mechanisms, the effect of repetitive pitching on the elbow load magnitude and variability was investigated. In addition, we explored whether repetitive pitching affects elbow muscle activation during pitching. Fifteen pitchers threw each 60 to 110 balls. The external valgus torque and electromyography of three elbow muscles were quantified during each pitch. Linear mixed model analyses were performed to investigate the effect of repetitive pitching. On a group level, the linear mixed models showed no significant associations of repetitive pitching with valgus torque magnitude and variability and elbow muscle activity. Significant differences exist between pitchers in their individual trajectories in elbow valgus torque and muscle activity with repetitive pitching. This shows the importance of individuality in relation to repetitive pitching. In order to achieve effective elbow injury prevention in baseball pitching, individual characteristics of changes in elbow load and muscle activity in relation to the development of UCL injuries should be investigated.Biomechanical EngineeringBiomechatronics & Human-Machine Contro

    Magnitude and variability of individual elbow load in repetitive baseball pitching

    No full text
    In baseball pitchers the elbow is exposed to high and repetitive loads (i.e. external valgus torque), caused by pitching a high number of balls in a practice session or game. This can result in overuse injuries like the ulnar collateral ligament (UCL) injury. To understand injury mechanisms, the effect of repetitive pitching on the elbow load magnitude and variability was investigated. In addition, we explored whether repetitive pitching affects elbow muscle activation during pitching. Fifteen pitchers threw each 60 to 110 balls. The external valgus torque and electromyography of three elbow muscles were quantified during each pitch. Linear mixed model analyses were performed to investigate the effect of repetitive pitching. On a group level, the linear mixed models showed no significant associations of repetitive pitching with valgus torque magnitude and variability and elbow muscle activity. Significant differences exist between pitchers in their individual trajectories in elbow valgus torque and muscle activity with repetitive pitching. This shows the importance of individuality in relation to repetitive pitching. In order to achieve effective elbow injury prevention in baseball pitching, individual characteristics of changes in elbow load and muscle activity in relation to the development of UCL injuries should be investigated.</p

    Obtaining wheelchair kinematics with one sensor only? The trade-off between number of inertial sensors and accuracy for measuring wheelchair mobility performance in sports

    No full text
    In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1°/s for wheelchair angular velocity when compared with the reference system. The two-IMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0°/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available.Biomechanical EngineeringBiomechatronics & Human-Machine Contro

    Obtaining wheelchair kinematics with one sensor only? The trade-off between number of inertial sensors and accuracy for measuring wheelchair mobility performance in sports

    No full text
    In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1°/s for wheelchair angular velocity when compared with the reference system. The two-IMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0°/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available

    Energy flow through the lower extremities in high school baseball pitching

    No full text
    It is generally accepted that most of the energy transferred to the ball during a baseball pitch is generated in the trunk and lower extremities. Therefore, purpose of this study was to assess the energy flow through the lower extremities during a baseball pitch. It was hypothesised that the (stabilising) leading leg mainly transfers energy in a distal-to-proximal order as a kinetic chain while the (driving) trailing leg generates most energy, primarily at the hip. A joint power analysis was used to determine the rates of energy (power) transfer and generation in the ankles, knees, hips and lumbosacral joint (L5-S1) for 22 youth pitchers. Analyses showed that the leading leg mainly transfers energy upwards in a distal-to-proximal order just before stride foot contact. Furthermore, energy generation was higher in the trailing leg and primarily arose from the trailing hip. In conclusion, the legs contribute differently to the energy flow where the leading leg acts as an initial kinetic chain component and the trailing leg drives the pitch by generating energy. The actions of both legs are combined in the pelvis and passed on to the subsequent, more commonly discussed, open kinetic chain starting at L5-S1.Support Biomechanical EngineeringBiomechanical Engineerin

    Quantifying Within-Individual Elbow Load Variability in Youth Elite Baseball Pitchers and Its Role in Overuse Injuries

    No full text
    Medial elbow overuse injuries are rising in baseball. The external valgus torque magnitude is a possible risk factor for medial elbow injuries. The magnitude on its own cannot explain why one pitcher sustains an injury and another does not. Therefore, the aim of this study is to describe the within-individual external valgus torque variability and to determine whether the within-individual external valgus torque variability can be described by a Gaussian distribution. Eleven youth elite baseball pitchers threw twenty-five fastball pitches. Body kinematics were measured with VICON motion capture at 400 Hz. Elbow valgus torques of the total 270 pitches were calculated with a custom-made inverse dynamic model in Python. Visual inspection and the Shapiro–Wilk test were performed to test for the within-individual elbow valgus torque normality. The results showed that within-individual valgus torque variability was present in pitchers and differed among pitchers. Furthermore, it was shown that the within-individual valgus torque variability was normally distributed in nine out of eleven subjects. In conclusion, the presence of and differences in within-individual elbow load variability among baseball pitchers can be useful variables as they might be related to overuse elbow injuries.Biomechanical EngineeringBiomechatronics & Human-Machine Contro

    Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept

    No full text
    Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restrict the athlete's natural movement due to equipment attachment. Therefore, the feasibility and validity of a more applicable method, requiring only a single standard camera for the recordings, combined with a deep-learning model and musculoskeletal model is evaluated in the present study during upper-body strength exercises performed by five athletes. Comparison of muscle forces obtained by the single camera driven model against those obtained from a state-of-the art marker-based driven musculoskeletal model revealed strong to excellent correlations and reasonable RMSD's of 0.4–2.1% of the maximum force (Fmax) for prime movers, and weak to strong correlations with RMSD's of 0.4–0.7% Fmax for stabilizing and secondary muscles. In conclusion, a single camera deep-learning driven model is a feasible method for muscle force analysis in a strength training environment, and first validity results show reasonable accuracies, especially for prime mover muscle forces. However, it is evident that future research should investigate this method for a larger sample size and for multiple exercises.Biomechanical EngineeringBiomechatronics & Human-Machine Contro

    Quantifying Within‐Individual Elbow Load Variability in Youth Elite Baseball Pitchers and Its Role in Overuse Injuries

    No full text
    Medial elbow overuse injuries are rising in baseball. The external valgus torque magnitude is a possible risk factor for medial elbow injuries. The magnitude on its own cannot explain why one pitcher sustains an injury and another does not. Therefore, the aim of this study is to describe the within‐individual external valgus torque variability and to determine whether the within-individual external valgus torque variability can be described by a Gaussian distribution. Eleven youth elite baseball pitchers threw twenty‐five fastball pitches. Body kinematics were measured with VICON motion capture at 400 Hz. Elbow valgus torques of the total 270 pitches were calculated with a custom‐made inverse dynamic model in Python. Visual inspection and the Shapiro–Wilk test were performed to test for the within‐individual elbow valgus torque normality. The results showed that within‐individual valgus torque variability was present in pitchers and differed among pitchers. Furthermore, it was shown that the within‐individual valgus torque variability was normally distributed in nine out of eleven subjects. In conclusion, the presence of and differences in within-individual elbow load variability among baseball pitchers can be useful variables as they might be related to overuse elbow injuries
    corecore