scholarly journals Shadow pitching deviates ball release position: kinematic analysis in high school baseball pitchers

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shigeaki Miyazaki ◽  
Go Yamako ◽  
Koji Totoribe ◽  
Tomohisa Sekimoto ◽  
Yuko Kadowaki ◽  
...  
Keyword(s):  
Motion Capture ◽  
Shoulder Joint ◽  
Male Students ◽  
Healthy Male ◽  
Joint Angles ◽  
Ball Release ◽  
Joint Task ◽  
Joint Forces ◽  
Position Task ◽  
Throwing Motion

Abstract Background Although shadow pitching, commonly called “towel drill,” is recommended to improve the throwing motion for the rehabilitation of pitching disorders before the initiation of a throwing program aimed at returning to throwing using a ball, the motion differs from that of normal throwing. Learning improper motion during ball release (BR) may increase shoulder joint forces. Abnormal throwing biomechanics leads to injures. However, there has been no study of shadow pitching focusing on the BR position. The purpose of the present study was to evaluate the BR position and kinematic differences between shadow pitching and normal throwing. In addition, the effect of setting a target guide for BR position on throwing motion was examined in shadow pitching. Methods The participants included in this study were 20 healthy male students who were overhand right-handed pitchers with no pain induced by a throwing motion. Participants performed normal throwing (task 1), shadow pitching using a hand towel (task 2), and shadow pitching by setting a target of the BR position (task 3). A motion capture system was used to evaluate kinematic differences in throwing motions, respectively. Examination items comprised joint angles and the differences in BR position. Results BR position of task 2 shifted significantly toward the anterior, leftward, and downward directions compared with task 1. The distance of BR position between tasks 1 and 2 was 24 ± 10%. However, task 3 had decreased BR deviation compared with task 2 (the distance between 3 and 1 was 14 ± 7%). Kinematic differences were observed among groups at BR. For shoulder joint, task 2 showed the highest value in abduction and horizontal adduction among groups. In spine flexion, left rotation and thorax flexion, task 2 was significantly higher than task 1. Task 3 showed small differences compared with task 1. Conclusions The BR position of shadow pitching deviated significantly in the anterior, leftward, and downward directions compared with normal throwing. Furthermore, we demonstrated that the setting of BR target reduces this deviation. Thus, the target of BR position should be set accurately during shadow pitching exercises in the process of rehabilitation.

Muscle torques and joint accelerations provide more sensitive measures of post-stroke movement deficits than joint angles.

2021 ◽  
Author(s):  
Ariel B Thomas ◽  
Erienne V Olesh ◽  
Amelia Adcock ◽  
Valeriya Gritsenko
Keyword(s):  
Motor Control ◽  
Motion Capture ◽  
Reaching Movements ◽  
Motor Deficits ◽  
Joint Angles ◽  
Active Joint ◽  
Joint Torques ◽  
Post Stroke ◽  
Muscle Moments ◽  
Muscle Torques

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The impact of stroke on the complex multi-joint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested whether post-stroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of post-stroke motor deficits than joint angles. The motion of twenty-two participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamics analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multi-joint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that muscle torques characterize individual reaching movements with higher information content than joint angles do. Moreover, muscle torques enable distinguishing the individual motor deficits caused by aging or stroke from the typical differences in reaching between healthy individuals. Similar results were obtained using metrics derived from joint accelerations. This novel quantitative assessment method may be used in conjunction with home-based gaming motion-capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions.

In-vivo three-dimensional motion analysis of the wrist during dart-throwing motion after midcarpal fusion and radioscapholunate fusion

2020 ◽  
Vol 45 (5) ◽  
pp. 501-507
Author(s):  
Lisa Reissner ◽  
Olga Politikou ◽  
Gabriella Fischer ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Three Dimensional ◽  
Healthy Controls ◽  
Dart Throwing ◽  
Radioscapholunate Fusion ◽  
Infrared Cameras ◽  
Basic Motion ◽  
Throwing Motion

We recorded the dart-throwing motion and basic motion tasks in patients following radioscapholunate fusion and midcarpal fusion with a three-dimensional motion capture system in vivo, using digital infrared cameras to track the movement of reflective skin markers on the hand and forearm. During the dart-throwing motion, 20 healthy volunteers showed a median range of motion of 107°. As expected, patients had significantly reduced wrist range of motion during basic motion tasks and dart-throwing motion compared with the healthy controls, except for ulnar flexion occurring in the dart-throwing motion in patients treated by midcarpal fusion and radial deviation after midcarpal fusion or radioscapholunate fusion. In addition, patients who had undergone radioscapholunate fusion had significantly reduced range of motion during dart-throwing motion compared with patients after midcarpal fusion.

IN VIVO SHOULDER JOINT FORCES AT ISOLATED MOTIONS

2008 ◽  
Vol 41 ◽  
pp. S144 ◽  
Author(s):  
Peter Westerhoff ◽  
Antonius Rohlmann ◽  
A. Bender ◽  
Friedmar Graichen ◽  
Georg Bergmann
Keyword(s):  
Shoulder Joint ◽  
Joint Forces

Shoulder joint forces in wheelchair propulsion

1993 ◽  
Vol 26 (3) ◽  
pp. 347
Author(s):  
Gale M. Gehlsen ◽  
Rafael Bahamonde
Keyword(s):  
Shoulder Joint ◽  
Wheelchair Propulsion ◽  
Joint Forces

scholarly journals In Vivo Measurement of Wrist Movements during the Dart-Throwing Motion Using Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5623
Author(s):  
Gabriella Fischer ◽  
Michael Alexander Wirth ◽  
Simone Balocco ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Mean Absolute Difference ◽  
Inertial Measurement ◽  
Dart Throwing ◽  
Measurement Units ◽  
Throwing Motion

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion–extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial–ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed.

scholarly journals DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

2021 ◽  
Author(s):  
Md Sanzid Bin Hossain ◽  
Joseph Drantez ◽  
Hwan Choi ◽  
Zhishan Guo
Keyword(s):  
Neural Networks ◽  
Motion Capture ◽  
Daily Living ◽  
Sagittal Plane ◽  
Human Motion ◽  
Joint Kinematics ◽  
Weighted Averaging ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Joint Angles

<div>Measurement of human body movement is an essential step in biomechanical analysis. The current standard for human motion capture systems uses infrared cameras to track reflective markers placed on the subject. While these systems can accurately track joint kinematics, the analyses are spatially limited to the lab environment. Though Inertial Measurement Unit (IMU) can eliminate the spatial limitations of the motion capture system, those systems are impractical for use in daily living due to the need for many sensors, typically one per body segment. Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles. Our developed algorithm estimates hip, knee, and ankle angles in the sagittal plane using two shoe-mounted IMU sensors in different practical walking conditions: treadmill, level overground, stair, and slope conditions. Specifically, we proposed five deep learning networks that use combinations of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) as base learners for our framework. Using those five baseline models, we proposed a novel framework, DeepBBWAE-Net, that implements ensemble techniques such as bagging, boosting, and weighted averaging to improve kinematic predictions. DeepBBWAE-Net predicts joint kinematics for the three joint angles under all the walking conditions with a Root Mean Square Error (RMSE) 6.93-29.0% lower than base models individually. This is the first study that uses a reduced number of IMU sensors to estimate kinematics in multiple walking environments.</div>

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