A musculoskeletal model of the shoulder region, which uses kinematic data as an input an estimates muscle forces and joint loads as an output, can give valuable information on the pitching motion. This helps us to get more insight into the pitching motion and its biomechanical interactions and may also help in reducing the injury risk and increasing pitching velocity. Currently, there are three problems that impede proper simulations: the lack of proper kinematic recordings, the fact that maximum force of the model could be too limited and the extreme character of the motion.
An experimental study was performed to create a dataset including upper limb kinematics and PCSA scaling factors to scale maximum force in the DSEM. An acromion cluster was used to track the scapula. PCSA scaling factors ranged from 1.11 to 2.02.
Following the experimental study, a case study was performed simulating this dataset in the DSEM. During simulation, the main problem arose in the optimization of the clavicle and scapula angles relative to the thorax, because the optimized angles contained large jumps, which are not realistic. This impeded proper simulation, because the jumps in muscle length caused both unsolved frames in the kinematics as well as in the dynamic model. Using a soft constraint instead of a hard constraint reduced these jumps and allowed for a complete solution in the kinematic model and an increase in the number of frames solved by the dynamic model. PCSA scaling also increased the number of frames solved by the dynamic model, however still unsolved frames were present, even after extreme scaling. Because of a change in range of motion as reported for pitchers, optimum muscle length might be different. This has a large impact in the model. If this would be the case, scaling optimum muscle length is recommended. In addition, segment scaling used in combination with using the soft constraint is recommended to improve the match between input angles and optimized angles, while still being compatible to the model.
To study the motion, the kinematic model of the DSEM was used to estimate muscle length and velocity for all muscles during the pitching motion. Comparing these values to the force-velocity and the active force-length relationship showed whether muscles were limited by one or both of these relationships to produce force. This was the case for the teres minor, triceps (all three heads), infraspinatus, anconeus and serratus anterior. The triceps showed a ‘stretch effect’, meaning shortening in the acceleration phase preceded by lengthening in the cocking phase. This means that there is a possibility for elastic energy to be stored for this muscle.

The recent advancements in inertial sensors technology and its promising results in motion tracking, catch the expert’s eyes to these new horizons in sports engineering. In baseball, which is the interest of this study, almost 90% of the pitchers got injured once a year due to wrong training and pitching techniques. Screening and real-time feedback to players would help them to improve their training procedures and safely increase their pitching performance.
In the last decades many researches have been carried out on employing this new tool in field measurements, instead of the common marker-based motion tracking with their complexity for the in field measurements. Although it is promising that inertial sensors are the future of motion tracking systems in this area, there are still many technical issues like, IMU sensors measurement limit, drift and bias. Besides in human motion tracking which involves multiple IMU sensors, coinciding the sensors and defining a global coordinate system requires substantial concerns.
This study focuses on developing a valid motion tracking method for the baseball pitchers, having a marker-based motion capture measurement as the reference. In order to be able to do this first of all the two systems was needed to be synchronized and at the same time be able to record the same motion. Secondly, the measurements should be defined in the same coordinate system. For this purpose, a simple functional calibration method has been developed and applied on both systems. This method is validated against a previous method (Seel, Schauer et al. 2012). Finally, The kinematic results are estimated at joint and segment’s angles, velocities and accelerations levels. The joint and segment’s angles computed by IMU sensors are validated based on marker-based measurements. The sensitivity of IMU-based measurements in estimating the angular velocity and acceleration of movements with different rate of movements (slow vs. fast) is investigated. It has been observed that for baseball pitching applications, IUM sensors with less mass and wider range of measurements are required.
In order to compare the dynamics of the human body, a scalable anthropometric model from the literature is used to define the mass and inertia properties of the segments. An inverse dynamics method is used to compute the kinetics energy and finally the power flow in the segments and joints. Again, all these results from the IMU measurements are compared with the Marker-based method. The advantages and disadvantages of the IMU according to these results are discussed to establish a practical protocol for future measurements and data analysis. One of the major issues in the dynamic analysis is that for translating the velocity from IMU to human body, the measurement protocol needs to provide a known starting and ending velocity. This is done by starting and ending the measurements from a standing position.
The method of this project can be used in baseball pitching motion tracking using the suggested protocol improvements and more advanced IMU sensors.