Rajiv Ranganathan

Kinetic Comparison Among the Fastball, Curveball, Change-up, and Slider in Collegiate Baseball Pitchers

Background Controversy exists about whether breaking pitches are more stressful than are fastballs. Previous biomechanical studies compared kinematics but not kinetics. Hypothesis Elbow and shoulder forces and torques are statistically different among the fastball, curveball, change-up, and slider. Study Design Descriptive laboratory study. Methods Twenty-one healthy collegiate pitchers were studied with a high-speed automated digitizing system. All subjects threw fastballs (n = 21), most threw curveballs (n = 20) and change-ups (n = 19), and a few threw sliders (n = 6). Wrist, elbow, and shoulder kinetics were calculated using inverse dynamics. Nine kinetic and 26 kinematic parameters were compared among the different pitch types using repeated-measures analysis of variance. Results At the shoulder, internal rotation torque, horizontal adduction torque, abduction torque, and proximal force were significantly less in the change-up than in the other 3 pitches. Shoulder horizontal adduction torque was greater in the fastball than in the curveball and slider. Shoulder proximal force was greater in the slider than in the curveball. Elbow proximal force was less in the change-up than in the other 3 pitches. Elbow varus torque was greater in the fastball and curveball than in the changeup. Elbow flexion torque was greater in the curveball than in the change-up. The curveball and change-up demonstrated kinematic differences from the fastball, consistent with previous studies. Conclusion There were significant kinematic differences between the fastball and curveball but few kinetic differences. The change-up had lower joint kinetics, lower angular velocities, and different body positions than the other 3 pitch types had. Results for the slider were inconclusive because of small sample size. Clinical Relevance Because the resultant joint loads were similar between the fastball and curveball, this study did not indicate that either pitch was more stressful or potentially dangerous for a collegiate pitcher. The low kinetics in the change-up implies that it is the safest.

Active robotic training improves locomotor function in a stroke survivor

BackgroundClinical outcomes after robotic training are often not superior to conventional therapy. One key factor responsible for this is the use of control strategies that provide substantial guidance. This strategy not only leads to a reduction in volitional physical effort, but also interferes with motor relearning.MethodsWe tested the feasibility of a novel training approach (active robotic training) using a powered gait orthosis (Lokomat) in mitigating post-stroke gait impairments of a 52-year-old male stroke survivor. This gait training paradigm combined patient-cooperative robot-aided walking with a target-tracking task. The training lasted for 4-weeks (12 visits, 3 × per week). The subject’s neuromotor performance and recovery were evaluated using biomechanical, neuromuscular and clinical measures recorded at various time-points (pre-training, post-training, and 6-weeks after training).ResultsActive robotic training resulted in considerable increase in target-tracking accuracy and reduction in the kinematic variability of ankle trajectory during robot-aided treadmill walking. These improvements also transferred to overground walking as characterized by larger propulsive forces and more symmetric ground reaction forces (GRFs). Training also resulted in improvements in muscle coordination, which resembled patterns observed in healthy controls. These changes were accompanied by a reduction in motor cortical excitability (MCE) of the vastus medialis, medial hamstrings, and gluteus medius muscles during treadmill walking. Importantly, active robotic training resulted in substantial improvements in several standard clinical and functional parameters. These improvements persisted during the follow-up evaluation at 6 weeks.ConclusionsThe results indicate that active robotic training appears to be a promising way of facilitating gait and physical function in moderately impaired stroke survivors.

Influence of Augmented Feedback on Coordination Strategies

The authors examined the influence of knowledge of results (KR) and concurrent feedback (ConFB) on coordination strategies in learning a 2-finger discrete force-production task. In Experiment 1, 4 groups learned the task under the following feedback regimes: ConFB, ConFB with knowledge of no-KR test (ConFB test information), KR after every trial (100% KR), and KR after every alternate trial (50% KR). Results show that the ConFB group had lower errors during acquisition but the highest errors in the no-KR transfer test. An uncontrolled manifold analysis showed that participants in the ConFB group adopted a strategy that tended to use multiple solutions to achieve the goal during acquisition, but they could not retain this strategy in the no-KR test. Both KR groups retained the same coordination strategy from acquisition into the transfer test, even though this strategy was not conducive to producing a constant force. In Experiment 2, the authors observed these differences even when the time-to-peak force between the groups was constrained to a criterion bandwidth. These results show that ConFB facilitates using different solutions from trial to trial to achieve the same goal, and this may be a reason for poor performance when feedback is removed.

Changing up the routine: intervention-induced variability in motor learning.

&NA; Variability is often introduced by an external agent (e.g., an instructor) during practice with the purpose of enhancing motor learning. Using a task analysis approach, we provide a framework to examine the effects of intervention-induced variability. We propose that variability may have markedly different consequences on learning depending on the task level at which it is introduced.

A Pilot Study on the Feasibility of Robot-Aided Leg Motor Training to Facilitate Active Participation

Robot-aided gait therapy offers a promising approach towards improving gait function in individuals with neurological disorders such as stroke or spinal cord injury. However, incorporation of appropriate control strategies is essential for actively engaging the patient in the therapeutic process. Although several control algorithms (such as assist-as-needed and error augmentation) have been proposed to improve active patient participation, we hypothesize that the therapeutic benefits of these control algorithms can be greatly enhanced if combined with a motor learning task to facilitate neural reorganization and motor recovery. Here, we describe an active robotic training approach (patient-cooperative robotic gait training combined with a motor learning task) using the Lokomat and pilot-tested whether this approach can enhance active patient participation during training. Six neurologically intact adults and three chronic stroke survivors participated in this pilot feasibility study. Participants walked in a Lokomat while simultaneously performing a foot target-tracking task that necessitated greater hip and knee flexion during the swing phase of the gait. We computed the changes in tracking error as a measure of motor performance and changes in muscle activation as a measure of active subject participation. Repeated practice of the motor-learning task resulted in significant reductions in target-tracking error in all subjects. Muscle activation was also significantly higher during active robotic training compared to simply walking in the robot. The data from stroke participants also showed a trend similar to neurologically intact participants. These findings provide a proof-of-concept demonstration that combining robotic gait training with a motor learning task enhances active participation.

The body-machine interface: A new perspective on an old theme

ABSTRACT Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer interfaces to bionic limbs, have had important consequences for people with movement disorders. The authors provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. They outline the steps involved in building such an interface and highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation.

Online feedback and the regulation of degrees of freedom in motor control.

We tested the hypothesis that the degree to which online feedback is used to control movement influences the regulation of degrees of freedom in a task. Ten participants performed an isometric force production task with their two index fingers with the goal of matching the total force to a target waveform. The role of online feedback was manipulated by changing three factors--the tracking mode, the profile of the target waveform, and the visual gain. The results showed that the coupling between the finger forces was lower in conditions where participants used online feedback to correct their movements compared to conditions where more feedforward strategies were used. The availability of online feedback is dependent on the nature of the task and this contributes to task-dependent changes in the regulation of the degrees of freedom.

Learning Redundant Motor Tasks with and without Overlapping Dimensions: Facilitation and Interference Effects

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills.

Influence of motor learning on utilizing path redundancy

We examined the role of motor learning in influencing the utilization of path redundancy in an interception task. Participants used a pen on a digitizing tablet with the goal of moving to intercept a stationary target shown on a computer screen. Concurrent visual feedback of the cursor and knowledge of results were provided. The changes in spatial variability after extended practice and the correlations between spatial locations on the path were consistent with the view that path redundancy was utilized. However, paths also became less variable with practice, indicating that less of the solution space was utilized. There was also a sequential relation in the movement paths from trial to trial with a tendency to use more similar movement paths on successive trials. These results show that models that account for both redundancy and invariance may be better able to capture the changes in variability with learning. Further, the sequential dependence in the paths suggests that the exploration of redundant solutions is a systematic search process.

Extracting synergies in gait: using EMG variability to evaluate control strategies

There has been extensive debate as to whether muscle synergies in motor tasks reflect underlying neural organization or simply correlations in muscle activity that are imposed by the task. One possible means of distinguishing these two alternatives is through the analysis of variability in the electromyogram (EMG). Here, we simulated EMG in eight lower-limb muscles and introduced hypothetical neural coupling between specific muscle groups. Neural coupling was simulated by introducing correlations in the neural activation commands to different muscles (positive, negative, or zero coupling). When the entire EMG signal was used for analysis, the extracted synergies reflected only simultaneous muscle activity, regardless of the neural coupling between the muscles. On the other hand, examining the variability in the EMG after subtracting the ensemble average was successful in identifying the simulated neural coupling. The extracted synergies from these two methods were also different when we analyzed data from participants during treadmill walking. The results emphasize the importance of examining EMG variability to understand the neural basis of muscle synergies.