Movement pattern recognition in basketball free-throw shooting

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant's class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1-score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross-movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross-movement analysis and the artificial generation of larger amounts of data.

Muscle activation time and free-throw effectiveness in basketball

This study attempts to analyze the relationship between free-throw efficiency and the time of arm muscle activation in players from 3 basketball teams with different levels of experience was investigated. During the experiment each player made 20 free throws during which the activation time of his right and left biceps and triceps brachii muscles were measured with the use of surface electromyography and high-speed cameras. Significant differences in muscle activation time (t) during a free throw were found between the groups of basketball players (p = 0.038) (novices: t = 0.664 ± 0.225 s, intermediate-level players: t = 1.15 ± 0.146 s, experts: t = 1.01 ± 0.388 s). In the right triceps brachii muscle in expert basketball players the coefficient of variation (CV) amounted to 44.60% at 81% efficiency, and in novices to 27.12% at 53% efficiency. The time of arm muscle activation during a free throw and its fluctuations vary along with the training experience of basketball players. In all studied groups of players, the variability of muscle activation time in accurate free throws is greater than in inaccurate free throws. Free-throw speed is irrelevant for free-throw efficiency.

Can Individual Movement Characteristics Across Different Throwing Disciplines Be Identified in High-Performance Decathletes?

Although the individuality of whole-body movements has been suspected for years, the scientific proof and systematic investigation that individuals possess unique movement patterns did not manifest until the introduction of the criteria of uniqueness and persistence from the field of forensic science. Applying the criteria of uniqueness and persistence to the individuality of motor learning processes requires complex strategies due to the problem of persistence in the learning processes. One approach is to examine the learning process of different movements. For this purpose, it is necessary to differentiate between two components of movement patterns: the individual-specific component and the discipline-specific component. To this end, a kinematic analysis of the shot put, discus, and javelin throwing movements of seven high-performance decathletes during a qualification competition was conducted. In total, joint angle waveforms of 57 throws formed the basis for the recognition task of individual- and discipline-specific throwing patterns using a support vector machine. The results reveal that the kinematic throwing patterns of the three disciplines could be distinguished across athletes with a prediction accuracy of up to 100% (57 of 57 throws). In addition, athlete-specific throwing characteristics could also be identified across the three disciplines. Prediction accuracies of up to 52.6% indicated that up to 10 out of 19 throws of a discipline could be assigned to the correct athletes, based on only knowing these athletes from the kinematic throwing patterns in the other two disciplines. The results further suggest that individual throwing characteristics across disciplines are more pronounced in shot put and discus throwing than in javelin throwing. Applications for training and learning practice in sports and therapy are discussed. In summary, the chosen approach offers a broad field of application related to the search of individualized optimal movement solutions in sports.

Ankle torque forecasting using time-delayed neural networks

A method for ankle torque prediction ahead of the current time is proposed in this paper. The mean average value of EMG signals from four muscles, alongside the joint angle and angular velocity of the right ankle, were used as input parameters to train a time-delayed artificial neural network. Data collected from five healthy subjects were used to generate the dataset to train and test the model. The model predicted ankle torque for five different future times from zero to 2 seconds. Model predictions were compared to torque calculated from inverse dynamics for each subject. The model predicted ankle torque up to 1 second ahead of time with normalized root mean squared error of less than 15 percent while the coefficient of determination was over 0.85.Clinical Relevance- the potential of the model for predicting joint torque ahead of time is helpful to establish an intuitive interaction between human and assistive robots. This model has application to assist patients with neurological disorders.

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

Influence of a pre-shot dynamic stretching routine on free throw performance

Study aim: The aim of the present study was to examine a single movement of dynamic stretching (SMDS) of the shooting arm as a pre-shot routine for free throw performance (FTP).

Material and methods: The sample consisted of 60 junior and senior basketball players from the youth league of Bosnia and Herzegovina (B&H), and the national level – the First Division of B&H. The authors found that some players during a game and training sessions apply an SMDS of the shooting arm as a pre-shot routine for FTP. Since previous literature suggests that length of the routine and pre-performance behaviors are quite different among players of all levels, the sample was divided based on the number of training hours per week (lower/higher number of training hours) and basketball experience (experienced/less experienced). The procedure involves every player performing five free throws (FTs) without prior stretching, five FTs immediately after SMDS of the triceps muscle and five FTs after SMDS of the m. flexor carpi radialis.

Results: After the first SMDS (m. triceps brachii) the percentage of FT slightly decreased in the first following attempt. After the second stretching (m. flexor carpi radialis), a significant FT percentage drop was noted in the first following FT for the whole sample. Players with more experience and more training hours per week had poorer results after the stretching.

Conclusions: The SMDS routine did not enhance the FTP and it had a rather harmful effect on FTP, especially SMDS of the flexor carpi radialis muscle, and the authors do not recommend SMDS before the FTP.

Prediction of shot success for basketball free throws: visual search strategy

In ball games, players have to pay close attention to visual information in order to predict the movements of both the opponents and the ball. Previous studies have indicated that players primarily utilise cues concerning the ball and opponents' body motion. The information acquired must be effective for observing players to select the subsequent action. The present study evaluated the effects of changes in the video replay speed on the spatial visual search strategy and ability to predict free throw success. We compared eye movements made while observing a basketball free throw by novices and experienced basketball players. Correct response rates were close to chance (50%) at all video speeds for the novices. The correct response rate of experienced players was significantly above chance (and significantly above that of the novices) at the normal speed, but was not different from chance at both slow and fast speeds. Experienced players gazed more on the lower part of the player's body when viewing a normal speed video than the novices. The players likely detected critical visual information to predict shot success by properly moving their gaze according to the shooter's movements. This pattern did not change when the video speed was decreased, but changed when it was increased. These findings suggest that temporal information is important for predicting action outcomes and that such outcomes are sensitive to video speed.

Temporal guidance of musicians' performance movement is an acquired skill

The ancillary (non-sounding) body movements made by expert musicians during performance have been shown to indicate expressive, emotional, and structural features of the music to observers, even if the sound of the performance is absent. If such ancillary body movements are a component of skilled musical performance, then it should follow that acquiring the temporal control of such movements is a feature of musical skill acquisition. This proposition is tested using measures derived from a theory of temporal guidance of movement, "General Tau Theory" (Lee in Ecol Psychol 10:221-250, 1998; Lee et al. in Exp Brain Res 139:151-159, 2001), to compare movements made during performances of intermediate-level clarinetists before and after learning a new piece of music. Results indicate that the temporal control of ancillary body movements made by participants was stronger in performances after the music had been learned and was closer to the measures of temporal control found for an expert musician's movements. These findings provide evidence that the temporal control of musicians' ancillary body movements develops with musical learning. These results have implications for other skillful behaviors and nonverbal communication.