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Background To investigate the effect of different fixation strategies of the intramedullary interlocking nail (IIN) on the mechanical behavior of a polymeric implant applied for femoral fracture fixation in calves, and to evaluate the performance of a glass fiber-reinforced polymer applied in a bovine femoral fracture reduction system, five Holstein male animals with a mean weight (±SD) of 62.8 ± 20.4 kg and aged 74 ± 15 were used to generate biomechanical parameters for this study. Twelve models of the fractured bovine femur, simulating a simple oblique fracture, were developed for use during the simulations. The models were divided into three groups, with each group of four models being associated with a different fixation strategy. Models were used to simulate the loading conditions corresponding to a calf in the transition (decubitus position to static position) condition. The maximum stresses found in each set (bone/implant) were compared with the reference stresses of each nail material. Results Maximum implant stresses were found in the screws and at the interface between the screw and the nail. The performance of implants was influenced by the material and fixation strategy, which can be confirmed by the stress values found in the set. The analysis indicated that the composite nail is able to withstand the loading demands in all fixation strategies. Conclusions The finite element analysis (FEA) demonstrated that all polymeric materials analyzed provided sufficient resistance to withstand the loading forces imposed to the femur when an adequate blocking strategy was applied.

Pedaling asymmetry is claimed as a factor of influence on injury and performance. However, the evidence is still controversial. Most previous studies determined peak torque asymmetries, which in our understanding does not consider the pattern of movement like torque profiles. Here we demonstrate that asymmetries in pedaling torque at different exercise intensities can be better described when the torque profiles are considered using functional analysis of variance than when only the peak values are analyzed. We compared peak torques and torque curves recorded while cyclists pedaled at submaximal intensities of 60%, 80%, and 95% of the maximal power output and compared data between the preferred and non-preferred legs. ANOVA showed symmetry or rather no difference in the amount of peak torque between legs, regardless of pedaling intensity. FANOVA, on the other hand, revealed significant asymmetries between legs, regardless of cycling intensity, apparently for different sections of the cycle, however, not for peak torque, either. We conclude that pedaling asymmetry cannot be quantified solely by peak torques and considering the analysis of the entire movement cycle can more accurately reflect the biomechanical movement pattern. Therefore, FANOVA data analysis could be an alternative to identify asymmetries. A novel approach as described here might be useful when combining kinetics assessment with other approaches like EMG and kinematics and help to better understand the role of pedaling asymmetries for performance and injury risks.

Asymmetries compromise performance in powerlifting and Paralympic powerlifting, but its quantification can be complex. Previous studies consider average or peak values to quantify asymmetries, however this approach does not consider the pattern of movement like velocity profiles. Here we demonstrate that conducting a functional analysis of variance (FANOVA) permits to quantify asymmetries in bench press performance by Paralympic powerlifting at different submaximal intensities. Kinematic data were collected from 10 Paralympic powerlifting athletes performing in bench press at submaximal intensities (50% and 90% of the one-repetition maximum). Linear velocity was quantified considering mean values and the entire waveform. Mean values were compared by analysis of variance (ANOVA) and the waveforms were compared by FANOVA. FANOVA identified asymmetry profiles that ANOVA did not recognize at the highest intensity, which is the closest to a competition. This way, FANOVA can bring advantages to the analysis of competitive performance. FANOVA data analysis identifies asymmetries at higher intensity of effort considering the whole pattern of movement. Therefore, we consider that the FANOVA's approach may benefit the biomechanical assessment of the Paralympic powerlifting.

The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

Lower extremity kinetic data during walking of 12 people with chronic poststroke were reanalyzed, using functional analysis of variance (FANOVA). To perform the FANOVA, the whole curve is represented by a mathematical function, which spans the whole gait cycle and avoids the need to identify isolated points, as required for traditional parametric analyses of variance (ANOVA). The power variables at the ankle, knee, and hip joints, in the sagittal plane, were compared between two conditions: With and without walking sticks at comfortable and fast speeds. For the ankle joint, FANOVA demonstrated increases in plantar flexion power generation during 60–80% of the gait cycle between fast and comfortable speeds with the use of walking sticks. For the knee joint, the use of walking sticks resulted in increases in the knee extension power generation during 10–30% of the gait cycle. During both speeds, the use of walking sticks resulted in increased power generation by the hip extensors and flexors during 10–30% and 40–70% of the gait cycle, respectively. These findings demonstrated the benefits of applying the FANOVA approach to improve the knowledge regarding the effects of walking sticks on gait biomechanics and encourage its use within other clinical contexts.