Background: Force platforms are widely used in biomechanics to measure ground reaction forces (GRF) during various human movements. However, traditional force plates are not easily used outside a research lab. To overcome this issue, researchers and manufacturers are developing low-cost portable force platforms that can be used in a variety of settings, including outdoors. Purpose: To validate the kinetic data obtained from a pair of portable K-Deltas force platforms compared to gold standard platforms fixed in the lab and to examine the measurement reliability between this pair of portable force platforms. Methods: Force-time curves from known masses, countermovement vertical jumps, and balance tests were used to assess validity of K-Deltas using a pair of Bertec force plates as a gold standard and between the K-Deltas pair of plates. Bland-Altman plots were used to evaluate the differences between K-Deltas and Bertec force plates. For the assessment of countermovement vertical jumps, impulse, peak rate of force development and peak force were calculated for both instruments and checked for agreement between instruments. Three young adults (2 male, 1 female, 25.4±0.83 years) participated in the study. Results: The percentage of Bland-Altman plot point within the limits of agreement was 94.59 % for the comparison between K-Deltas and Bertec and 94.83% between the pair of K-Deltas. Conclusion: The results show that the portable force platforms could be utilized successfully for assessing pertinent parameters in clinical and sports biomechanics. The findings suggest that portable force platforms can be used as an alternative to traditional laboratory equipment for field assessment, providing significant improvements compared to the past. Level of Evidence: Level 3

Background: Handheld dynamometers are used for diagnosis and rehabilitation at several pathologies. Grip force is essential for a person’s quality of life. Most dynamometers are not equipped with rehabilitation or training protocols, although the bibliography highlights this need. Objective: To compare the validity and reliability of Jamar and K-Force Grip dynamometers in patients with shoulder impingement syndrome and healthy people. Methods: Concurrent validity was assessed using known weights. Forty-four (44) patients and 69 healthy adults performed three maximum repetitions in random order with each hand, at each instrument. Reliability was evaluated using ICCs for each instrument separately, and between the two instruments. The differences between the two dynamometers were evaluated using repeated t-tests. Results: Concurrent validity of the two instruments did not reveal fix or proportional bias. Analyses for reliability yield high correlation coefficients for both groups with the lower one being between the two instruments r = 0.90, p < 0.05, (0.72–0.91) for the healthy group. T-tests showed that all participants had lower force values when using the K-Force Grip (p < 0.05). Conclusion: Both dynamometers were reliable in measuring hand grip strength for both groups. K-Force Grip was reliable and valid with respect to Jamar that was used as a benchmark. The different handle dimensions between the two dynamometers may be the reason for the different force values.

Background: The quantification of electromyographic activity using surface electrodes is invaluable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in participants with stroke-related movement disorders in functional situations. Methods: Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman’s correlation coefficients (SCs), linear correlation coefficients (LCCs), and cross-correlation coefficients (CCCs) were calculated. Results: The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable-walking condition, an SC of 0.894 ± 0.091 and an LCC of 0.909 ± 0.094 were noted. In the fast-walking condition, an SC of 0.918 ± 0.064 and an LCC of 0.935 ± 0.056 were observed. For the 1 min sit-to-stand test, an SC of 0.880 ± 0.058 and an LCC of 0.881 ± 0.065 were noted. Conclusions: This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.

This study aimed to examine countermovement jump (CMJ) kinetic data using logistic regression, in order to distinguish sports-related mechanical profiles. Eighty-one professional basketball and soccer athletes participated, each performing three CMJs on a force platform. Inferential parametric and nonparametric statistics were performed to explore group differences. Binary logistic regression was used to model the response variable (soccer or not soccer). Statistical significance (p < 0.05) was reached for differences between groups in maximum braking rate of force development (RFD_Dmax, U₇₉ = 1035), mean braking rate of force development (RFD_Davg, U₇₉ = 1038), propulsive impulse (IMP_U, t₇₉ = 2.375), minimum value of vertical displacement for center of mass (S_BCMmin, t₇₉ = 3.135), and time difference (% of impulse time; Δ_Τ) between the peak value of maximum force value (F_Umax) and S_BCMmin (U₇₉ = 1188). Logistic regression showed that RFD_Davg, impulse during the downward phase (IMP_D), IMP_U, and Δ_Τ were all significant predictors. The model showed that soccer group membership could be strongly related to IMP_U, with the odds ratio being 6.48 times higher from the basketball group, whereas RFD_Davg, IMP_D, and Δ_Τ were related to basketball group. The results imply that soccer players execute CMJ differently compared to basketball players, exhibiting increased countermovement depth and impulse generation during the propulsive phase.

The aim of the present study was to examine the sensitivity of several movement features during running to exhaustion in a subject-specific setup adopting a cross-sectional design and a machine learning approach. Thirteen recreational runners, that systematically trained and competed, performed an exhaustive running protocol on an instrumented treadmill. Respiratory data were collected to establish the second ventilatory threshold (VT2) in order to obtain a reference point regarding the gradual accumulation of fatigue. A machine learning approach was adopted to analyze kinetic and kinematic data recorded for each participant, using a random forest classifier for the region pre and post the second ventilatory threshold. SHapley Additive exPlanations (SHAP) analysis was used to explain the models’ predictions and to provide insight about the most important variables. The classification accuracy value of the models adopted ranged from 0.853 to 0.962. The most important feature in six out of thirteen participants was the angular range in AP axis of upper trunk C7 (RT_APu) followed by maximum loading rate (RFD_maxD) and the angular range in the LT axis of the C7. SHAP dependence plots also showed an increased dispersion of predictions in stages around the second ventilatory threshold which is consistent with feature interactions. These results showed that each runner used the examined features differently to cope with the increase in fatigue and mitigate its effects in order to maintain a proper motor pattern.

We explored the impact of running in the severe intensity domain on running mechanics and muscle oxygenation in competitive runners by investigating the relationship between mechanical deviations from typical stride characteristics and muscle oxygen saturation (SmO₂) in the quadriceps muscle. Sixteen youth competitive runners performed an 8‐min exhaustive running test on an outdoor track. Running mechanics were continuously monitored using inertial measurement units. Rectus femoris SmO₂ and total hemoglobin (a measure of blood volume) were continuously monitored by near‐infrared spectroscopy. One‐class support vector machine (OCSVM) modeling was employed for subject‐specific analysis of the kinematic data. Statistical analysis included principal component analysis, ANOVA, and correlation analysis. Mechanical deviations from typical stride characteristics increased as the running test progressed. Specifically, the percentage of outliers in the OCSVM model rose gradually from 2.2 ± 0.8% at the start to 43.6 ± 28.2% at the end (p < 0.001, mean ± SD throughout). SmO₂ dropped from 74.3 ± 8.4% at baseline to 10.1 ± 6.8% at the end (p < 0.001). A moderate negative correlation (r = −0.61, p = 0.013) was found between the average SmO₂ and the percentage of outlier strides during the last 15% of the run. During high‐intensity running, alterations in running biomechanics may occur, linked to decreased quadriceps muscle oxygenation. These parameters highlight the potential of using running kinematics and muscle oxygenation in training to optimize performance and reduce injury risks. Our research contributes to understanding biomechanical and physiological responses to endurance running and emphasizes the importance of individualized monitoring.