Monitoring training load in youth soccer players: effects of a six-week preparatory training program and the association between external and internal loads.

This study examined the effects of a six-week preparatory training program on physical performance and physiological adaptations in junior soccer players. Additionally, we investigated whether a relationship existed between external and internal loads. Youth soccer players (aged 16 years old) from a youth football academy participated in six weeks of pre-conditioning training. Wireless Polar Team Pro and Polar heart rate sensors (H10) were used to monitor physical performance indicators (sprint and acceleration scores, covered distance, maximum and average speed and duration), physiological responses (maximum and average heart rate [HR] and R-R interval, time in HR zones 4+5, and heart rate variability [HRV]), and training load score. Additionally, muscle status and rating of perceived exertion (RPE) scores were measured using digital questionnaires. Significant increases were observed in the majority of physical performance indicators [i.e., sprints (p = 0.015, ES = 1.02), acceleration (p = 0.014, ES = 1), total distance (p = 0.02, ES = 0.87), as well as maximum speed (p = 0.02, ES = 0.87)]. A trend towards improvement was observed in the remaining performance indicators (i.e., distance/min and avg speed; ES = 0.6), training load (ES = 0.2), muscle status (ES = 0.3)), and all physiological responses parameters (ES = 0.1 to 0.6). Significant correlations were found between the majority of external load parameters (i.e., performance indicators) and objective (i.e., physiological responses) and subjective (i.e., RPE, muscle status) internal load parameters (p < 0.001). The highest number of moderate-large correlations were registered between performance indicators and time in HR zone 4+5 (0.58 < r < 0.82), training load (0.53 < r < 0.83), average HR (0.50 < r < 0.87), maximal HR (0.51 < r < 0.54) and average R-R interval (0.58 < r < 0.76). HR zone 4+5, average and maximal HR, average R-R interval, and training load score may help control training parameters and reduce the risk of under- or over-training in youth soccer players. However, these conclusions should be confirmed and replicated in future studies with more diverse subject populations.

Internal Parameters Calibration of Vision Sensor and Application of High Precision Integrated Detection in Intelligent Welding Based on Plane Fitting.

Vision sensing is a key technology to realize on-line detection of welding groove sizes and welding torch relative position and posture parameters during the arc welding process of intelligent production. For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method for its internal parameters is proposed firstly, which improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor. Then, the high precision integrated detection algorithms are derived for the V-groove size parameters and the spatial position and posture (SPP) parameters of the welding torch relative to the welding groove based on a single modulated laser lines image. The algorithms make full use of the data in a single modulated laser lines image, adopting data segmentation and plane fitting to realize the 3D reconstruction of V-groove surfaces and its adjacent workpiece surfaces of planar workpiece, so solving the parameters with high precision. In the verification tests, the relative detection error of V-groove size parameters of planar workpiece is less than 1%, and the relative detection error of SPP parameters of welding torch relative to the welding groove is less than 5%, which separately shows the effectiveness and accuracy of the calibration method and the detection algorithms. This research work provides a good technical support for the practical application of the specially designed vision sensor in the intelligent welding production.