Biomechanical analysis of barefoot walking and three different sports footwear in children aged between 4 and 6 years old.

The technological transformation and advertising utilized in the footwear industry significantly impact purchasing decisions. The gait properties, barefoot and with shoes, change depending on the footwear structure. The aim of this work is the biomechanical analysis of walking barefoot and with different sports shoes in a controlled group of 12 children between 4 and 6 years old. Kinematic and spatiotemporal variables were analyzed using a BTS motion capture analysis system with the Helen Hayes protocol. Previously, a survey was carried out with 262 families with children between 4 and 6 years old to justify the choice of footwear for this study. No significant differences were found between any of the measured conditions. The kinematic results showed significant differences in the ankle (right sagittal plane p = 0.04, left p < 0.01; right frontal plane p < 0.01, left p < 0.01), knee (right and left sagittal plane p < 0.01) and hip (right sagittal plane p < 0.01, left p = 0.04; right frontal plane p = 0.03). Additionally, the post hoc analysis revealed significant differences between barefoot gait and different footwear. The footwear used for this study and each one's various characteristics are not preponderant in the spatiotemporal and kinematic parameters of the children's gait. Thus, the footwear purchase may be conditioned by its design or composition and other properties may not be relevant.

Traces of muscular fatigue in the rectus femoris identified with surface electromyography and wavelets on normal gait.

Background: Although the median frequency is used as a basis to assess the presence of muscular fatigue, some studies have found that it is not a good estimator for this purpose. Physiotherapists often deal with patients with movement issues related to fatigue. A new objective estimator that is easy to obtain and interpret can help with the management of such patients.Objective and Purpose: The aim of this study was to identify the frequency bands most affected by fatigue and propose a protocol to obtain such information.Methods: Thirty healthy subjects (age 22.05 ± 2.42 years, height 1.71 ± 0.08 m, and weight 68.75 ± 11.42 kg) were recruited to measure the rectus femoris electromyography (EMG) of both legs during gait, before and after a fatiguing protocol. Four frequency bands were studied: high frequency (107-250 Hz), midfrequency (65-94 Hz), 40 Hz (34-53 Hz), and low frequency (< 25 Hz). Each frequency band is related to different aspects of the muscular contraction.Results: The main finding is that the ratios of the 40-Hz frequency and low-frequency bands showed significant differences in both legs between pre-fatigue and post-fatigue recordings. Critical thresholds F* 4.86 and 4.93 were exceeded at 4.5-8.7%, 30.64-46%.38, 63.75-66.8%, and 83.69-90.79% and at 0-2.53%, 4.7-15.21%, 18.15-54.65%, and 55.81-99.95% of the gait cycle, respectively, with p values < .05.Conclusions: The low-frequency band constitutes the best indicator to assess fatigue in gait.

Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder.

The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07-272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance.

Improving the staging of neck injuries using a new index, the Neck Functional Holistic Analysis Score: Clustering approach to determine degrees of impairment.

BACKGROUND: Traumatic cervical spine injuries are amongst the traffic injuries that can cause most harm to a person. Classifying subtypes of clinical presentations has been a method used in other pathologies to diagnose more efficiently and to address the appropriate treatment and the prognosis. The management of patients suffering from cervical injuries could be improved by classifying the severity of the impairment. This will allow clinicians to propose better treatment modalities according to the severity of the injury. MATERIALS AND METHODS: The present study is a retrospective cohort study performed with the clinical data from 772 patients stored at Fisi-(ON) Health Group. All the patients treated for cervical spine injuries are evaluated using the EBI-5® system, which is based on inertial measurement unit (IMU) technology. The normalized range of motion of each patient was incorporated into a single index, the Neck Functional Holistic Analysis Score (NFHAS). RESULTS: Clustering analysis of the patients according to their NFHAS resulted in five groups. The Kruskal-Wallis H test showed that there were statistically relevant differences in the ROM values and NFHAS of the patients depending on the cluster they were assigned to: FE X2(4) = 551.59, p = 0.0005; LB ROM X2(4) = 484.58, p = 0.0005; RT ROM X2(4) = 557.14, p = 0.0005; NFHAS X2(4) = 737.41, p = 0.0005. Effect size with ηp2 for the comparison of groups were: FE = 0.76, LB = 0.68, RT = 0.76 and NFHAS = 0.96. CONCLUSION: The NFHAS is directly correlated to the available ROM of the patient. The NFHAS serves as a good tool for the classification of cervical injury patients. The degree of impairment shown by the cervical injury can now be staged correctly using this new classification.