Physical activity analysis assessment has been a concern throughout human history. The intersection of technological growth with sports has given rise to a burgeoning field known as sports engineering. In the 19th century, the advent of chrono-photography and pioneering marked the inception of sports performance analysis. In recent years, the noticeable developments achieved in wearable low-power electronics with wireless high interconnection capability, as a part of modern technologies, have aided us in studying sports parameters such as motor behavior, biomechanics, equipment design, and materials science, playing an essential role in the understanding of sports dynamics. This study aims to review over 250 published articles since 2018, focusing on utilizing and validating these emergent technologies in sports and clinical aspects. It is predicted that one of the next steps in sports technology and engineering development will be using algorithms based on artificial intelligence to analyze the measurements obtained by multi-sensor systems (sensor fusion) to monitor biometric and physiological parameters in performance analysis and health assessments.

Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced athletes. This study aims to validate the use of a single inertial measurement system to assess some relevant technical parameters of amateur players. Among other things, we attempt to search for significant correlations between the flexion extension and torsion of the torso and the lateral distance of the ball from the body at the instant of impact. This research involved a group of amateur players who performed a series of standardized gestures (forehands and serves) wearing a sensorized chest strap fitted with a wireless inertial unit. The collected data were processed to extract performance metrics. The percentage coefficient of variation for repeated measurements, Wilcoxon signed-rank test, and Spearman’s correlation were used to determine the system’s reliability. High reliability was found between sets of measurements in all of the investigated parameters. The statistical analysis showed moderate and strong correlations, suggesting possible applications in assessing and optimizing specific aspects of the technique, like the player’s distance to the ball in the forehand or the toss in the serve. The significant variations in technical execution among the subjects emphasized the need for tailored interventions through personalized feedback. Furthermore, the system allows for the highlighting of specific areas where intervention can be achieved in order to improve gesture execution. These results prompt us to consider this system’s effectiveness in developing an on-court mobile application.

Accurate observation of specific athletes' movements can assist coaches in assessing sporting gestures and addressing specific technical flaws, thereby enhancing overall performance. In addition, the system could support the biomechanical study in case of those technical personalisms that could lead to excessive overloading of the muscle-tendon and joint apparatus, resulting in an increased risk of injury. In this paper we propose the use of a recently developed system that employs artificial intelligence techniques to provide a reliable estimation of human body pose, opening up new possibilities for sports-related applications.

Pickleball has gained popularity across diverse age groups. This sport has particular balls that require different hitting styles, like hitting dinks. This study focuses on introducing pickleball players' kinematic analysis through a MediaPipe-based deep learning (DL) tool and analyzes the dominant leg's femur angle, knee angle, and wrist motion of pickleball players during the hitting dink shots, comparing pickleball players with high-level and beginner levels. Fourteen male pickleball players (aged 46.5 ± 10.5) participated in performing a dink shot during warm-up while being recorded by a GoPro camera and analysed by the DL tool. Statistical analysis, including T-tests and One-way ANOVA, showed significant differences between athletes and non-athletes in femur angle during the dink shot (p < 0.001), where high-level athletes demonstrated more femur flexion. Knee angles did not differ significantly, but advanced athletes maintained continuous wrist motion after the ball hit (p < 0.001). The MediaPipe-based DL tool estimated joint angles and motion patterns, offering an approximate alternative to visual analysis by coaches. With the developed DL tool, the coaches and players can rapidly monitor kinematics parameters and identify improvement areas. Future studies should further investigate foot positioning and trunk rotations in different shot types to assess pickleball biomechanical behaviours.

In Paralympic sports, to guarantee fair competition, it is necessary to identify those peculiar abilities that characterize the discipline and the motor limitations that may or may not most affect the athlete’s performance in a specific sports task, assigning an appropriate classification to the level of impairment. This study proposes a minimally invasive assessment system based on a single inertial sensor to support the investigation of the peculiarities of water polo with disabilities by analyzing players’ trunk inclinations during a simulated match and angular speeds in swimming tests. By comparing a small group of athletes of various classes and those without disabilities, we intended to evaluate whether athletes with lower limb disabilities may be disadvantaged compared to athletes with upper limb disabilities. The results suggest no difference in the mean percentage of time in vertical and horizontal positions when comparing players with and without disabilities, although specific impairments led to distinct behaviors (Δ = 0.9%, p = 0.841). Interesting insights emerged in swimming and turning situations in the water. Strong correlations (r > 0.7, p < 0.05) were found between swimming performance metrics and classification points. Furthermore, players with spasticity exhibited lower smoothness in turning movements, suggesting less fluid execution than those with other impairments affecting the same limbs. These findings highlight the IMU system’s potential to provide objective, quantitative data for refining WPA classification protocols.

Background/Objectives: In tennis, timing plays a crucial factor as it influences the technique and effectiveness of strokes and, therefore, matches results. However, traditional technical evaluation methods rely on subjective observations or video motion-tracking technology, mainly focusing on spatial components. This study evaluated the reliability of an acoustic detection system in analyzing key temporal elements of the game, such as the rally rhythm and timing of strokes. Methods: Based on a machine learning algorithm, the proposed acoustic detection system classifies the sound of the ball’s impact on the racket and the ground to measure the time between them and give immediate feedback to the player. We performed trials with expert and amateur players in controlled settings. Results: The ML algorithm showed a detection accuracy higher than 95%, while the average accuracy of the whole system that was applied on-court was 85%. Moreover, this system has proven effective in evaluating the technical skills of a group of players on the court and highlighting their areas for improvement, showing significant potential for practical applications in player training and performance analysis. Conclusions: Quantitatively assessing timing offers a new perspective for coaches and players to improve performance and technique, providing objective data to set training regimens and optimize game strategies.