For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called ‘Cosinussº One’, which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k-nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.

Aerobic adaptations following interval training are supposed to be mediated by increased local blood supply. However, knowledge is scarce on the detailed relationship between exercise duration and local post-exercise blood supply and oxygen availability. This study aimed to examine the effect of five different exercise durations, ranging from 30 to 240 s, on post-exercise muscle oxygenation and relative changes in hemoglobin concentration. Healthy male subjects (N = 18) performed an experimental protocol of five exercise bouts (30, 60, 90, 120, and 240 s) at 80 % of peak oxygen uptake V . O 2 peak $$ \left({\overset{.}{\mathrm{V}}\mathrm{O}}_{2\mathrm{peak}}\right) $$ in a randomized order, separated by 5-min recovery periods. To examine the influence of aerobic fitness, we compared subjects with gas exchange thresholds (GET) above 60 % V . O 2 peak $$ \overset{.}{\mathrm{V}}{\mathrm{O}}_{2\mathrm{peak}} $$ (GET60+) with subjects reaching GET below 60 % V . O 2 peak $$ \overset{.}{\mathrm{V}}{\mathrm{O}}_{2\mathrm{peak}} $$ (GET60−). V . O 2 $$ \overset{.}{\mathrm{V}}{\mathrm{O}}_2 $$ and relative changes in concentrations of oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin were continuously measured with near-infrared spectroscopy of the vastus lateralis muscle. Post-exercise oxygen availability and local blood supply increased significantly until the 90-s exercise duration and reached a plateau thereafter. Considering aerobic fitness, the GET60+ group reached maximum post-exercise oxygen availability earlier (60 s) than the GET60− group (90 s). Our results suggest that (1) 90 s has evolved as the minimum interval duration to enhance local oxygen availability and blood supply following cycling exercise at 80 % V . O 2 peak $$ \overset{.}{\mathrm{V}}{\mathrm{O}}_{2\mathrm{peak}} $$ ; whereas (2) 60 s is sufficient to trigger the same effects in subjects with GET60 + .

Introduction: As survival of previously considered as lethal congenital heart disease forms is the case in our days, issues regarding quality of life including sport and daily activities emerge. In patients with Fontan circulation, there is no pump to propel blood into the pulmonary arteries since the systemic veins are directly connected to the pulmonary arteries. The complex hemodynamics of Fontan circulation include atrial function, peripheral muscle pump, integrity of the atrioventricular valve, absence of restrictive, or obstructive pulmonary lung function. Therefore, thoracic mechanics are of particular importance within the complex hemodynamics of Fontan circulation. Methods: To understand the physiology of respiratory muscles, the aim of this study was to examine the matching of auxiliary respiratory muscle oxygen delivery and utilization during incremental exercise in young male Fontan patients (n = 22, age = 12.04 ± 2.51) and healthy Controls (n = 10, age = 14.90 ± 2.23). All subjects underwent a cardiopulmonary exercise test (CPET) to exhaustion whereas respiratory muscle oxygenation was measured non-invasively using a near-infrared spectrometer (NIRS). Results: CPET revealed significantly lower peak power output, oxygen uptake and breath activity in Fontan patients. The onset of respiratory muscle deoxygenation was significantly earlier. The matching of local muscle perfusion to oxygen demand was significantly worse in Fontans between 50 and 90% $\stackrel{.}{\text{V}}{\text{O}}_{2\text{peak}}$. Findings: The results indicate that (a) there is high strain on respiratory muscles during incremental cycling exercise and (b) auxiliary respiratory muscles are worse perfused in patients who underwent a Fontan procedure compared to healthy Controls. This might be indicative of a more general skeletal muscle strain and worse perfusion in Fontan patients rather than a localized-limited to thoracic muscles phenomenon.

In high-intensity interval training the interval duration can be adjusted to optimize training results in oxygen uptake, cardiac output, and local oxygen supply. This study aimed to compare these variables in two interval trainings (long intervals HIIT3m: 3 min work, 3 min active rest vs. short intervals HIIT30s: 30 s work, 30 s active rest) at the same overall work rate and training duration. 24 participants accomplished both protocols, (work: 80% power output at VO₂peak, relief: 85% power output at gas exchange threshold) in randomized order. Spirometry, impedance cardiography, and near-infrared spectroscopy were used to analyze the physiological stress of the cardiopulmonary system and muscle tissue. Although times above gas exchange threshold were shorter in HIIT3m (HIIT3m 1669.9 ± 310.9 s vs. HIIT30s 1769.5 ± 189.0 s, p = 0.034), both protocols evoked similar average fractional utilization of VO₂peak (HIIT3m 65.23 ± 4.68% VO₂peak vs. HIIT30s 64.39 ± 6.78% VO₂peak, p = 0.261). However, HIIT3m resulted in higher cardiovascular responses during the loaded phases (VO₂p < 0.001, cardiac output p < 0.001). Local hemodynamics were not different between both protocols. Average physiological responses were not different in both protocols owning to incomplete rests in HIIT30s and large response amplitudes in HIIT3m. Despite lower acute cardiovascular stress in HIIT30s, short submaximal intervals may also trigger microvascular and metabolic adaptions similar to HIIT3m. Therefore, the adaption of interval duration is an important tool to adjust the goals of interval training to the needs of the athlete or patient.

In weightlifting, quantitative kinematic analysis is essential for evaluating snatch performance. While marker-based (MB) approaches are commonly used, they are impractical for training or competitions. Markerless video-based (VB) systems utilizing deep learning-based pose estimation algorithms could address this issue. This study assessed the comparability and applicability of VB systems in obtaining snatch kinematics by comparing the outcomes to an MB reference system. 21 weightlifters (15 Male, 6 Female) performed 2–3 snatches at 65%, 75%, and 80% of their one-repetition maximum. Snatch kinematics were analyzed using an MB (Vicon Nexus) and VB (Contemplas along with Theia3D) system. Analysis of 131 trials revealed that corresponding lower limb joint center positions of the systems on average differed by 4.7 ± 1.2 cm, and upper limb joint centers by 5.7 ± 1.5 cm. VB and MB lower limb joint angles showed highest agreement in the frontal plane (root mean square difference (RMSD): 11.2 ± 5.9°), followed by the sagittal plane (RMSD: 13.6 ± 4.7°). Statistical Parametric Mapping analysis revealed significant differences throughout most of the movement for all degrees of freedom. Maximum extension angles and velocities during the second pull displayed significant differences (p < .05) for the lower limbs. Our data showed significant differences in estimated kinematics between both systems, indicating a lack of comparability. These differences are likely due to differing models and assumptions, rather than measurement accuracy. However, given the rapid advancements of neural network-based approaches, it holds promise to become a suitable alternative to MB systems in weightlifting analysis.