An objective analysis of the human movement can help both clinical assessment and sports performance. Kinovea is a free 2D motion analysis software that can be used to measure kinematic parameters. This low-cost technology has been used in sports sciences, as well as in the clinical and research fields. One interesting tool is that it can measure an object (or person) passing in front of the camera, taking into account the perspective between the camera and the recorded object. Although it has been validated as a tool to assess time-related variables, few studies assessed its validity compared to a Gold Standard; furthermore, its reliability in different perspectives has not been previously assessed. The main objective of this study is to determine the validity of the Kinovea software compared to AutoCAD, and its intra and inter-rater reliability in obtaining coordinates data; a second objective is to compare their results at 4 different perspectives (90°, 75°, 60° and 45°) and to assess the inter and intra rater reliability at each perspective. For this purpose, a wire structure figure in the shape of a human lower limb was designed and measured in AutoCAD; it was then recorded during a pendular motion with a video-camera placed at distance of 5 m and analyzed with Kinovea in the 4 perspectives (90°, 75°, 60° and 45°). Each frame was examined by three observers who made two attempts. A multiple approach was applied involving the analysis of the systematic error, with a two-way ANOVA 2x4; the relative reliability with Intraclass Correlation Coefficient (ICC) and the Coefficient of Variance (CV) (95% confidence interval); and the absolute reliability with the Standard Error (SE). The results indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

Clinical rehabilitation and sports performance analysis both require the objectification of 12 movement. Kinovea © is a free 2D motion analysis software that enables the establishment of 13 kinematics parameters. This low-cost technology has been used in sports sciences, as well as

<p>Functional studies analyze the interaction between the human body and its environment studying both, the posture and the movement. It is well known that at the Universities where health science and sports studies are taught there is a need to raise academic activities involving functional studies.</p><p>The main objective is to make awareness of the factors that can modulate a certain function in the human body and must be well known for university students to perform a good research projects and career. To do this, we present a detailed list of intrinsic and extrinsic functional modulator factors (FMF) of the human body and propose a new methodology to teach those skills. During both, the project data collection and data reduction, FMF such as sitting and standing postures or walking, running and jumping movements must be taken into account. The recognition of the FMF that may influence the results must be previously known and identified to succeed in the methodological design of functional studies of the human body.</p>The novelty of our work is not to direct the attention to the lecturer but to focus it to the student’s academic support to accomplish the active work like the fieldwork presented in this paper. In conclusion, the understanding of the FMF by university students using fieldwork activities during their degree would help them to develop a critical attitude in terms of validity and scientific evidence.<p> </p><p> </p>

Background: Ground reaction forces are the gold standard for detecting gait events, but they are not always applicable in cerebral palsy. Ghoussayni's algorithm is an event detection method based on the sagittal plane velocity of heel and toe markers. We aimed to evaluate whether Ghoussayni's algorithm, using two different thresholds, was a valid event detection method in children with bilateral spastic cerebral palsy. We also aimed to define a new adaptation of Ghoussayni's algorithm for detecting foot strike in cerebral palsy, and study the effect of event detection methods on spatiotemporal parameters. Methods: Synchronized kinematic and kinetic data were collected retrospectively from 16 children with bilateral spastic cerebral palsy (7 males and 9 females; age 8.9 ± 2.7 years) walking barefoot at self-selected speed. Gait events were detected using methods: 1) ground reaction forces, 2) Ghoussayni's algorithm with a threshold of 0.5 m/s, and 3) Ghoussayni's algorithm with a walking speed dependent threshold. The new adaptation distinguished how foot strikes were performed (heel and/or toe) comparing the timing when the foot markers velocities fell below the threshold. Differences between the three methods, and between spatiotemporal parameters calculated from the two Ghoussayni's thresholds were analyzed. Findings: There were statistically significant (P < 0.05) differences between methods 1 and 3, and between some spatiotemporal parameters calculated from methods 2 and 3. Ghoussayni's algorithm showed better performance for foot strike than for toe off. Interpretation: Ghoussayni's algorithm using 0.5 m/s is valid in children with bilateral spastic cerebral palsy. Event detection methods affect spatiotemporal parameters.

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