Influence of static wrist orthosis on muscle activity and shoulder and elbow range of motion during a functional task: a biomechanical study

<p></p><p>ABSTRACT Orthoses are therapeutic resources that are appropriate to protect and remedy deformities or to help in the performance of certain functions; however, its use may lead to proximal compensations in the shoulder. Thus, this study aims to evaluate the influence of dorsal static 30° extension orthoses on the shoulder and elbow biomechanics in 25 asymptomatic individuals during a functional task. The range of motion and muscle activation was collected by simultaneous and synchronized analysis during the Elui functional test related to feeding, under the conditions with and without the orthosis. In order to allow a comparison of the different subjects and muscles, the data were analyzed by EMG signal of each muscle and, for kinematic analysis, pre-defined marker coordinate systems were constructed. The captured signals were filtered and processed by custom software, and the t-test for paired samples, SPSS® software, p<0.05, was used. We found significant increase in activation of the anterior deltoid and pectoralis major muscle in the reach phase and upper trapezius, anterior and posterior deltoid in the release phase with the orthosis. The kinematic analysis showed a significant increase in the range of motion of shoulder abduction movements, elbow flexion and pronation in the displacement phase and shoulder extension and elbow flexion movements in the release phase. Our findings suggest that the use of static wrist orthosis while performing a task can lead to compensations, with predominant activation of more proximal muscles of the upper limb.</p><p></p

The image shows a situation with high local efficiency of player 11 at the moment that he received the ball, having five options to make the pass (green edges).

This situation demonstrates that if he was undermarked (unable to receive the ball) this play would not be possible.</p

Analyzed data.

https://doi.org/10.6084/m9.figshare.19222746 (player position and tracking). (TXT)</p

The <i>X</i> axis represents the pass of time and the <i>Y</i> axis the players (value of graph metric), the pixels with light colors represent higher values for the player at that moment, while dark tones mean lower values.

The X axis represents the pass of time and the Y axis the players (value of graph metric), the pixels with light colors represent higher values for the player at that moment, while dark tones mean lower values.</p

Confusion matrices for each test round, where the first row shows the true positive and false negatives values respectively.

And the second row shows the false negative and true negative respectively.</p

The figure demonstrate how the entropy values for two players vary over time, where the <i>X</i> axis represents the seconds of ball possession and the <i>Y</i> axis is the entropy measure at the moment.

(b) shows the same values of item (a), but in the visual rhythm format, where light tones represent higher values of entropy and dark shades the lower values.</p

Visual rhythm arrangement used as input for classification models.

This representation gathers eight different complex network metrics obtained from the attacking and defending teams: (1) Centrality; (2) Clustering coefficient; (3) Eccentricity; (4) Entropy; (5) Global efficiency; (6) Local efficiency; (7) PageRank; and (8) Vulnerability. Where each metric was represented by a channel in the image, in this way each image has sixteen dimensions (one for each of the eight metrics of both teams). So the image is represented with 11 pixels height (one for each player), 167 pixels width (representing time), and 16 dimensions (one for metric).</p

The figure depicts a graph built from the (x, y)-coordinates of each player.

Each vertex has a circle surrounding it, and the circle sizes aid to compare the betweenness entropy of a vertex with the others, where the larger the circle, the greater the betweenness entropy.</p

Code utilized.

https://github.com/lstival/soccer_graph_classification (Code repository). (TXT)</p

Proposed method.

Given a set of soccer videos, the proposed method consists of detecting and tracking the players in the field toward obtaining their (x,y)-coordinates. Then, we divided the tracking data into training and testing subsets, using the k-fold cross-validation protocol. Next, we model the interaction between players using complex networks, in which the nodes represent players, while edges the chances of passing the ball to teammates. Thus, we extract representations for the built complex network and use the visual rhythms technique to summarize such features for a time window. Finally, we use the visual rhythms maps to build a classifier for predicting the changes of an attack move reaching the attack zone, and also an explainable method to estimate the contribution of input features.</p