Background: this study examined the effects of caffeine supplementation on anaerobic performance, neuromuscular efficiency and upper and lower extremities fatigue in Olympic-level boxers. Methods: Eight male athletes, members of the Spanish National Olympic Team, were enrolled in the study. In a randomized double-blind, placebo-controlled, counterbalanced, crossover design, the athletes completed 2 test sessions after the intake of caffeine (6 mg·kg⁻¹) or placebo. Sessions involved initial measures of lactate, handgrip and countermovement jump (CMJ) performance, followed by a 30-seconds Wingate test, and then final measures of the previous variables. During the sessions, electromiography (EMG) data were recorded on the gluteus maximus, biceps femoris, vastus lateralis, gastrocnemius lateral head and tibialis anterior. Results: caffeine enhanced peak power (6.27%, p < 0.01; Effect Size (ES) = 1.26), mean power (5.21%; p < 0.01; ES = 1.29) and reduced the time needed to reach peak power (−9.91%, p < 0.01; ES = 0.58) in the Wingate test, improved jump height in the CMJ (+2.4 cm, p < 0.01), and improved neuromuscular efficiency at peak power in the vastus lateralis (ES = 1.01) and gluteus maximus (ES = 0.89), and mean power in the vastus lateralis (ES = 0.95) and tibialis anterior (ES = 0.83). Conclusions: in these Olympic-level boxers, caffeine supplementation improved anaerobic performance without affecting EMG activity and fatigue levels in the lower limbs. Further benefits observed were enhanced neuromuscular efficiency in some muscles and improved reaction speed.

Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of inappropriate comparison techniques. On the contrary, DTW is a potential method that compensates for the sensor latency, and works sufficiently in comparison with certified systems.

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Recreational running (RR) is becoming a popular way to increase physical activity for improving health, together with a higher incidence of knee injuries. The aim was to analyze the effect of a four-week supplementation with a mixture of Harpagophytum procumbens, Zingiber officinale and Bixa orellana on males, middle-aged, RR with an undiagnosed knee discomfort. A randomized triple-blind placebo-control trial was conducted among male RR aged 40–60 years suffering from self-declared knee discomfort after training. Participants were assigned to supplementation (2 g/day in 6 doses; n = 13; intervention group (IG)) or matched placebo (n = 15; control group (CG)) for 4 weeks. At pre- and post-intervention, assessment of routine blood biomarkers, body composition, running biomechanics and body temperature was performed using standardized procedures. Machine learning (ML) techniques were used to classify whether subjects belonged to IG or CG. ML model was able to correctly classify individuals as IG or CG with a median accuracy of 0.857. Leg fat mass decreased significantly (p = 0.037) and a deeper reduction in knee thermograms was observed in IG (p < 0.05). Safety evaluation revealed no significant differences in the rest of parameters studied. Subjects belonging to IG or CG are clearly differentiated, pointing into an effect of the supplement of ameliorating inflammation.

(1) Background: this study aimed to determine if there are differences in quadriceps and hamstring muscle activation in professional male and female soccer players. (2) Methods: muscle activation was recorded by surface electromyography in 27 professional soccer players (19 male and 8 female). The players performed the Bulgarian squat and lunge exercises. Vastus medialis, vastus lateralis, rectus femoris, semitendinosus, and biceps femoris were the muscles analyzed. (3) Results: The statistical analysis of the hamstring:quadriceps ratio showed no significant differences (p > 0.05). Significant differences were found in the vastus medialis:vastus lateralis ratio for both the lunge exercise (t₂₀ = 3.35; p = 0.001; d = 1.42) and the Bulgarian squat (t₂₃ = 4.15; p < 0.001; d = 1.76). For the intragroup muscular pattern in the lunge and Bulgarian squat exercises, the female players showed higher activation for the vastus lateralis muscle (p < 0.001) than the male players and lower muscle activation in the vastus medialis. No significant differences were found in the rectus femoris, biceps remoris, and semitendinosus muscles (p > 0.05). (4) Conclusions: Differences were found in the medial ratio (vastus medialis: vastus lateralis). Moreover, regarding the intramuscular pattern, very consistent patterns have been found. In the quadriceps muscle: VM>VL>RF; in the hamstring muscle: ST>BF. These patterns could be very useful in the recovery process from an injury to return players to their highest performance.

The use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. the 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training and both have been studied in order to see which one achieves a better result. The artificial neural network can be trained either to obtain more accurate kinematic gait signals or to improve the gait descriptors obtained after initial processing. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly. the accuracy of the 3D camera to objectify human gait was measured before and after training. In both training approaches, a considerable improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best. supervised learning improves the accuracy of 3D cameras but the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis.