Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.

This study aimed at assessing physical performance of elite karatekas and non-karatekas. More specifically, effects of kumite and kata technique on joint mobility, body stability, and jumping ability were assessed by enrolling twenty-four karatekas and by comparing the results with 18 non-karatekas healthy subjects. Sensor system was composed by a single inertial sensor and optical bars. Karatekas are generally characterized by better motor performance with respect non-karatekas, considering all the examined factors, i.e., mobility, stability, and jumping. In addition, the two techniques lead to a differentiation in joint mobility; in particular, kumite athletes are characterized by a greater shoulder extension and, in general, by a greater value of preferred velocity to perform joint movements. Conversely, kata athletes are characterized by a greater mobility of the ankle joint. By focusing on jumping skills, kata technique leads to an increase of the concentric phase when performing squat jump. Finally, kata athletes showed better stability in closed eyes condition. The outcomes reported here can be useful for optimizing coaching programs for both beginners and karatekas based on the specific selected technique.

The importance of physical activity has been widely demonstrated both in clinics and in sports. One of the new frontier training programs is high-intensity functional training (HIFT). The immediate effects of HIFT on the psychomotor and cognitive performance of well-trained people are still not clear. This paper aims to evaluate the immediate effects induced by HIFT on blood lactate levels, physical performance in terms of body stability and jump ability, and cognitive performance in terms of reaction time. Nineteen well-trained participants were enrolled in the experimental studies and asked to execute six repetitions of a circuit training. Data were gathered both in a pre-training session and after each one of the circuit repetitions. An immediate significant increase with respect to the baseline was observed during the first repetition, with a further increase after the third one. No effects on jump ability were found, whereas a deterioration in body stability was found. Positive immediate effects on cognitive performance in terms of accuracy and speed in task execution were assessed. The findings can be exploited by trainers during coaching to optimize the design of training programs.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.

The possibility of measuring predictive factors to discriminate athletes at higher risk of anterior cruciate ligament (ACL) injury still represents an open research question. We performed an observational study with thirteen female basketball players who performed monopodalic jumps and single-leg squat tests. One of them suffered from an ACL injury after the first test session. Data gathered from twelve participants, who did not suffer from ACL injury, were used for a reliability analysis. Parameters related to leg stability, load absorption capability and leg mobility showed good-to-excellent reliability. Path length, root mean square of the acceleration and leg angle with respect to the vertical axis revealed themselves as possible predictive factors to identify athletes at higher risk. Results confirm that six months after reconstruction represents the correct time for these athletes to return to playing. Furthermore, the training of leg mobility and load absorption capability could allow athletes to reduce the probability of new injuries.