Introduction: Cyclograms are gait analysis tools that characterize the geometric aspect of the pattern of locomotion. Cyclograms are angle-angle diagrams that are very useful for representing cyclic patterns such as walking. This study is based on the hypothesis that parameters extracted from hip-knee cyclograms of individuals walking on a treadmill with 0° and 5° slopes can be used to determine the age group and sex of the volunteers. Methods: In total, 40 physically active healthy adult volunteers, 20 young people (10 of each gender) and 20 elderly (10 of each gender), were divided into 4 groups, and the average value of area (A), perimeter (P) and the ratio P/√A of cyclogram were calculated, as well as the speed and cadence. Results: The young male (YM) speeds were higher than the elderly male (EM) speeds (p=0.00), and the young female (YF) speeds were higher than the elderly female (EF) speeds (p=0.00). No difference in speed was found between YM and YF (p=0.59) or between EM and EF (p=0.95). The parameters extracted directly from the cyclogram allowed us to distinguish the studied groups according to age group (p<0.05), especially with the treadmill inclined at 5°, but it was not enough to determine gender (p>0.51). Conclusion: The hypothesis was partially confi rmed because parameters extracted from the hip-knee cyclograms could differentiate volunteers by age group but not gender.

Introduction: Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods: In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results: The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson's correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion: The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.

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