محمدحسين ذوالفقاري عبير

The analysis of movement events, including key phases such as gait initiation and termination, foot-ground contact, and kinematic and dynamic changes during motion, plays a crucial role in understanding movement patterns. It enables the precise assessment of dynamic changes in gait, contributing to biomechanics and human movement control. Many neurological disorders, such as stroke and Parkinson's disease, lead to pathological changes in movement patterns, while peripheral neural pathways often remain intact. Therefore, optimizing movement patterns in patients with foot drop and eva‎luating the biomechanical characteristics of movement in individuals with spinal cord injuries are key research objectives. This study follows an applied research methodology with broad applications in rehabilitation, biomedical engineering, robotics, and augmented reality systems. Functional Electrical Stimulation (FES) is used as an effective rehabilitation method for these patients. Accurate detection of movement events and real-time monitoring of gait phases are among the most critical factors for the successful implementation of this method. Two patients and two healthy individuals were selected as participants. selection criteria for patients included intact nerve and muscle function in the peroneal region and the ability to lift the foot off the ground through electrical stimulation. To automate this process, an inertial sensor-based system was designed and implemented. This system leverages advanced signal processing algorithms to analyze movement parameters such as angle, acceleration, and angular velocity, enabling movement control optimization. Additionally, dedicated software was developed for data collection, processing, and movement analysis, facilitating more precise eva‎luation of movement patterns and improved rehabilitation strategies. In this study, an algorithm was designed and implemented to detect toe-off events with 100% accuracy and a temporal precision of 3.05 milliseconds, and heel-strike events with 100% accuracy and a temporal precision of 66.45 milliseconds. Furthermore, two additional algorithms were developed to detect heel-off events. 1. Moreover, two software applications were implemented: LabVIEW for research purposes and QT for medical applications, alongside a motion monitoring device. The results of this study demonstrate that combining rule-based methods with precise movement data measurement can provide an effective framework for movement monitoring and rehabilitation optimization. These findings pave the way for the development of innovative assistive systems in rehabilitation and can play a significant role in improving the quality of life for individuals with movement disorders.

الگوي حركتي , افتادگي مچ پا , بيومكانيكي , توان‌بخشي , اختلالات حركتي

Movement Pattern , Foot drop , Biomechanics , Rehabilitation , Movement Disorders

Mohammad Hossein Zolfaghari Abir

Abas Erfanian Omidvar