The aim of this study was to analyze the concurrent validity, test–retest reliability, and capacity to detect changes of four different portable devices used to measure a wide range of neuromuscular parameters derived from countermovement jump (CMJ). An accelerometric device (Myotest), a jump mat (Ergojump), an optical device (Optojump), and a smartphone app (MyJump) were simultaneously examined for concurrent validity against gold-standard measures (motion-capture system and a force platform). Twenty-two CMJ-derived variables were collected from 15 healthy male subjects (n = 60 CMJs). Contraction time (CT) and eccentric duration (EccD) measurements obtained from the Myotest were moderately to largely associated with and not different from force platform measurements (r = 0.31 to 0.64, ES = 0.11 to 0.18) and showed moderate test-retest reliability (intraclass correlation coefficient (ICC) = 0.92 to 0.97, coefficient of variation (CV) = 3.8 to 8.0%). Flight time (FT) and jump height (JH) from Ergojump, Optojump, and MyJump showed moderate to strong associations with gold-standard measurements (r = 0.57 to 0.98) and good test–retest reliability (ICC = 0.54 to 0.97, CV = 1.8 to 4.2). However, all portable devices underestimated JH (ES = 1.25 to 2.75). Independent of the instrument used, the analyzed CMJ variables showed good capacity to detect changes (standard error of measurement (SEM) < smallest worthwhile change (SWC)), with the exception of rate of force and rate of power development parameters, which showed marginal capacity (SEM > SWC). The Myotest is preferable to measure temporal parameters during ground contact, whereas Ergojump, Optojump, and MyJump devices may be preferable to measure FT and JH, with the Optojump being the most accurate.

This study investigated the roles of growth, maturation, physical fitness, and technical skills on selection onto an under-14 years basketball team. The sample consisted of 150 male players, aged 13.3 ± 0.7 years, divided into selected (SE—top players chosen by coaching staff to form an elite regional team) and non-selected (NSE—remaining players) groups. Anthropometry, body composition, biological maturation, and training experience data were collected using standard procedures. Physical fitness was assessed using the Yo-Yo IE2, sit-ups, handgrip, squat jump, countermovement jump, 3 kg medicine ball throw, 20 m sprint, and T-Test. Technical skills were acquired using the American Alliance for Health, Physical Education, Recreation, and Dance (AAHPERD)’s basketball-specific test battery. Groups were compared using a Student’s t test and multivariate analysis of covariance (MANCOVA), with training experience and biological maturation as covariates. A forward stepwise discriminant function analysis was employed to identify variables that maximized the separation between groups. The results showed that SE players were taller, had greater fat-free mass, greater strength, power, and agility, and were technically more skillful compared with NSE players (p < 0.05) when controlling for training experience and maturation. It was also found that players were best discriminated by the 3 kg medicine ball throw and control dribble, revealing the importance of qualified training to achieve excellence in youth basketball. 92.7% of the basketballers were correctly classified into their original groups. It is therefore confirmed that the additional effects of training experience and biological maturation positively influenced the performance of young basketball players. We recommend that coaches focus not only on players’ body sizes, but also on their skill level, especially during adolescence, when selecting teams in order to promote sustainable long-term development.

WalkinSense is a new device designed to monitor walking. The aim of this study was to measure the accuracy and repeatability of the gait analysis performed by the WalkinSense system. Descriptions of values recorded by WalkinSense depicting typical gait in adults are also presented. A bench experiment using the Trublu calibration device was conducted to statically test the WalkinSense. Following this, a dynamic test was carried out overlapping the WalkinSense and the Pedar insoles in 40 healthy participants during walking. Pressure peak, pressure peak time, pressure-time integral, and mean pressure at eight-foot regions were calculated. In the bench experiments, the repeatability (i) among the WalkinSense sensors (within), (ii) between two WalkinSense devices, and (iii) between the WalkinSense and the Trublu devices was excellent. In the dynamic tests, the repeatability of the WalkinSense (i) between stances in the same trial (within-trial) and (ii) between trials was also excellent (ICC > 0.90). When the eight-foot regions were analyzed separately, the within-trial and between-trials repeatability was good-to-excellent in 88% (ICC > 0.80) of the data and fair in 11%. In short, the data suggest that the WalkinSense has good-to-excellent levels of accuracy and repeatability for plantar pressure variables.

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.

Aim: To quantify the physiological demands and impact of muscle function t of the Fran workout, one of the most popular CrossFit benchmarks. Methods: Twenty experienced CrossFitters—16 male: 29 (6) years old and 4 female: 26 (5) years old— performed 3 rounds (with 30-s rests in between) of 21–21, 15–15, and 9–9 front squats to overhead press plus pull-up repetitions. Oxygen uptake and heart rate were measured at baseline, during the workout, and in the recovery period. Rating of perceived exertion, blood lactate, and glucose concentrations were assessed at rest, during the intervals, and in the recovery period. Muscular fatigue was also monitored at rest and at 5 minutes, 30 minutes, and 24 hours postexercise. Repeated-measures analysis of variance was performed to compare time points. Results: Aerobic (52%–29%) and anaerobic alactic (30%–23%) energy contributions decreased and the anaerobic lactic contribution increased (18%–48%) across the 3 rounds of the Fran workout. Countermovement jump height decreased by 8% (−12 to −3) mean change (95% CI), flight duration by 14% (−19 to −7), maximum velocity by 3% (−5 to −0.1), peak force 4% (−7 to −0.1), and physical performance (plank prone 47% [−54 to −38]) were observed. Conclusions: It appears that the Fran workout is a physically demanding activity that recruits energy from both aerobic and anaerobic systems. This severe-intensity workout evokes substantial postexercise fatigue and corresponding reduction in muscle function.