Postural Stability in Athletes during Special Hurdle Tests without a Definite Dominant Leg

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

The impact of a multicomponent training program on physical fitness and joint injuries in Spanish male professional soccer players

This study aimed to assess the effects of a multicomponent training program (ProSAS) on selected measures of physical fitness and joint injuries in Spanish male professional soccer players. Twenty-six professional soccer players (age: 25.2 ± 3.1 years) were randomly assigned to either the experimental (EG; n = 16) or control group (CG; n = 10). Y-balance test (YBT), Single leg countermovement jump (SLCMJ), Single leg hop for distance test (SLH), Side-hop test (SH), Speedy jump test (SpJ), Agility T-test (TT) and Lower extremity functional test (LEFT) were performed before and after an 8-weeks training period. Additionally, any joint injury occurring during the intervention period were recorded. Significant improvements in the EG in all tests (p = 0.001 to p = 0.014), except for SLCMJ (p = 0.632) with the dominant leg were observed. CG displayed decreases in SLCMJ with the non-dominant leg (p = 0.014), SH for both legs (p = 0.001 to p = 0.050), TT (p = 0.005), and LEFT (p = 0.001). Inter-groups differences revealed better results for the EG in all variables (p = 0.001 to p = 0.008) except from SLCMJd (p = 0.555). Moreover, the EG exhibited lower joint injury incidence and burden compared to the CG (p < 0.05). These findings suggest that the ProSAS is an effective multicomponent program to improve variables related to risk of joint injuries, and consequently, to reduce the incidence and burden of these injuries in professional soccer players.

Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model.

A Velostat-Based Pressure-Sensitive Mat for Center-of-Pressure Measurements: A Preliminary Study

Center-of-pressure (CoP) displacements play a key role in studies assessing postural stability. The accepted instrument to measure CoP trajectories is the force platform, but pressure-sensitive mats (PSMs) are an alternative composed of a matrix of sensitive cells. A typical cell comprises two electrodes with piezoresistive material in between, while a force platform has a force sensor at each of its corners. In this paper, we compare a homemade Velostat-based PSM and an affordable commercial mat with a commercial force platform in a test series with 42 healthy volunteers in single-legged trials (29 males, 13 females; height 1.74 (0.09) m, weight 74.3 (16.34) kg, age 31.21 (12.66) years). The aim of the research was to perform a preliminary study of the performance of our prototype to measure CoP, and more specifically, the standard deviation of the CoP path on both axes, the medial-lateral and anterior-posterior. We could thus discover several improvements for future clinical applications. The intraclass correlation coefficient (ICC) for agreement in the base experiment showed a moderate value for the prototype (0.38 to 0.63) and lower values for the commercial mat (0.11 to 0.12). However, we identified several factors that were relevant to improve ICC and reduce error by considering several processing options: (i) the known crosstalk problem between cells that appears in this kind of mats must be eliminated; (ii) the response time of the sensor has to be taken into account; and (iii) increasing the mat resolution also improves agreement. Therefore, as future work, we plan to test the improved version of the prototype in a clinical environment.