Special Issue on Performance Analysis in Sport and Exercise

The analysis of human performance has always aroused great interest from s sport scientists and, more recently, the clinical field. In elite sports, aspects that contribute to performance can be found in the athlete’s physiological strength and power, technique, strategy, and mental toughness. Since victory is often a matter of a few details, it is becoming increasingly critical to regularly monitor specific biomechanical parameters to fine-tune both technique and training programs in order to achieve incremental performance improvements and reduce the risk of injury.

In this context, modern electronic technology, thanks to the reduced power requirements and dimensions of the devices developed, is, therefore, less invasive, allowing the wide distribution of wearable, light, and easily interconnected devices (Internet of Things–IoT). Consequently, this has led to the development of new devices specifically designed for sports applications and often integrated into sportswear or incorporated into sports equipment. This wide availability of wearable sensors has led to the easier accessibility of different physiological, dynamic, and kinematic parameter data. Therefore, the extensive availability of these parameters, often easily accessible even from simple mobile or tablet apps, allows in-depth analyses of human movement to be performed quickly. Furthermore, many advanced technology systems initially developed exclusively for the sporting field have also proved effective in the clinical evaluation of motor gestures, to quantify the variability of daily motor gestures from reference physiological models in patients suffering from neurodegenerative pathologies. Among the evaluation systems that, in recent years, have shown significant improvement, we should also consider video analysis systems not only in 2D but also in 3D. Modern video cameras with a high-acquisition frequency (fps) and software systems based on deep learning techniques today allow for in-depth and non-invasive analyses of human movement. Therefore, the wide availability of biological and biomechanical parameters of human movement, obtained through increasingly less invasive and easy-to-implement evaluation and monitoring systems, has allowed the development of mathematical models of human performance that are increasingly close to the reality of the phenomenon analyzed. With this in mind, the main goal of this Special Issue is to address the existing knowledge on the methods and devices currently used to study and analyze human movement performance in sports and clinical fields. In sports, scientific publications reflect the wide range of approaches and technologies available and the growing interest in various sports. The scientific contributions in sport performance analysis reflect the wide range of methods exploiting the available technologies and the growing interest in their applications across several disciplines. In particular, as already mentioned, several technological systems can be used for the performance assessment of athletes; the most popular among them include video analysis systems, wearable devices, and tracking devices such as GPS (Global Positioning System). The latter is the most widely used, mainly due to its ease of use and relatively low cost. Due to its limited indoor satellite range, it is primarily used in outdoor sports, such as soccer [1]. GPS technology has provided specific insights into sub-elite youth football training load and recovery status to monitor training environments and load changes [2]. Inertial Measurement Sensors (IMUs) can measure the movements and orientation of an object in space, thanks to the availability of accelerometers, magnetometers, and gyroscopes. Using a system of IMUs, it was possible to effectively recognize the movements and evaluate the accuracy of the movement of a traditional Chinese sport (Baduanjin) [3].

IMUs have also been applied in the clinical domain; a study on ataxia-telangiectasia patients showed the application of wearable motion acquisition systems to assess motor skills through recursion, harmony, and symmetry measurements [4]. Even if GPS and IMU systems are small and easy devices, they must be worn by the athlete or subject to obtain measurements. In this context, these modern devices have been employed to produce mathematical models able to describe the characteristics of kayak stroke in terms of periodicity and average strength, and the effects of these on stroke frequency [5]. Similarly, in [6], traditional measurement procedures were exploited to define statistical prediction methods such as linear recommendation scores, logistic regression, discriminant analysis, and a neural network for identifying talents in tennis.

Differently from these, the video-analysis measurements are the least invasive, as they do not involve devices directly attached to the athlete or subject; however, this allows one to assess the movement only by a video recording with high-speed cameras. Using this technology, it was possible to record ball trajectories to analyze the throwing behavior (biomechanics, speed) of high-level players and athletes and to understand the technical differences of different ages. Specifically, the study described in [7] investigated the situational dependence of concrete environmental conditions (“constraints”) of successful throwing actions, and the biomechanics of throwing at the goal compared to the throwing speed of world-class water polo players under competition conditions. The study in [8], using a 3D video analysis, compared the biomechanical parameters of top Italian senior and youth shot put athletes with world-class athletes evaluated in previous investigations. Moreover, the semi-automatic InStat Fitness video system has made it possible to assess the running performance of footballers based on ball possession to determine its potential influence on the team’s results in the UEFA Champions League [9], and to classify performance according to the specific playing position [1].

Within the clinical field, a kinematic gait and motor coordination analysis was conducted on subjects affected by neurodegenerative diseases, especially in multiple sclerosis, by analyzing the joint angular displacement time and spatiotemporal parameters with a fusion method between surface EMG (sEMG) and a motion-capture system, using four optoelectronic cameras [10].

This Special Issue demonstrates a considerable extent of interest from the scientific community in the field, as evidenced by the high number of articles submitted. Of the seventeen articles submitted, only ten were accepted for publication after a rigorous peer-review process, with an acceptance rate of 59%, showing evidence of the growing interest in human performance analysis in sport and exercise.