Pitching shoulder passive flexibility: torque-angle analysis for external rotation and internal rotation

Effect of Kinematic Chain Exercise Protocol on Throwing Performance and Shoulder Muscle Strength among University Shot Put Athletes-A Randomized Controlled Trial

Background/Objectives: This study looks at how a kinematic chain exercise regimen that targets the lower, core, and upper body affects university shot put participants' shoulder muscle strength and throwing efficiency. This study fills an apparent research void on shot put training approaches by presenting a comprehensive kinematic chain workout program. It was anticipated that this method would improve performance the most, considering the complex biomechanical requirements of the sport. Methods: Eighty athletes aged (19.87 ± 1.31 years), were assigned into two groups at random: experimental (n = 40) and control (n = 40). While the control group carried on with their usual training, the experimental group participated in an 8-week kinematic chain training program. Pre- and post-training evaluations were carried out to evaluate shot put-throwing ability, shoulder muscle strength, and participant satisfaction with the exercise regimen. Results: The analyses were performed to evaluate the between- and within-group effects in the 10-week intervention period using a two-way ANOVA. This study demonstrated that, when compared to the control group, the athletes in the kinematic chain program had significantly increased throwing distance (p = 0.01) and shoulder muscle strength (p = 0.01). Furthermore, there was a significant increase (p = 0.005) in the athletes' satisfaction levels with the workout program among those in the experimental group. Conclusions: In shot put athletes, this study suggests that a kinematic chain-focused strategy can improve throwing performance and shoulder muscle strength. The findings suggest that incorporating kinematic chain workouts into shot put training programs could be beneficial. However, conclusions should be drawn with caution, and further research is necessary to confirm the effectiveness of kinematic chain-based approaches across various sports and to understand their broader implications in sports science.

Prediction of Passive Torque on Human Shoulder Joint Based on BPANN

In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient's joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists.