Student: Melanie Parke
Project Mentors: Dr. Claire Honeycutt – SBHSE
Dr. Xiaojun Tian – SBHSE
Dr. Sangram Redkar – ASU Polytechnic School
YouTube Link: View the video link below before joining the zoom meeting
Zoom Link: https://asu.zoom.us/j/97361426825
Zoom meeting time: 9am - 11am
Abstract
After a stroke, one of the most prevalent complications is gait abnormality. Altered walking patterns create a variety of difficulties for stroke survivors including unilateral weakness in extremities, unilateral movement abnormality, loss of sensory mechanisms, decreased gait speed and cadence, and foot drop. A common solution to address these issues is an ankle foot orthotic (AFO). Ankle foot orthotics have been widely prescribed and used to support proper gait pattern and decrease the risk of falls for stroke survivors. However, contradicting evidence has shown that AFOs do not necessarily prevent fall incidence as AFO users fall up to 2.2 times more often than non-users. A proposed solution to combat this is a “smart” ankle foot orthotic that can actively monitor gait pattern in real time and prevent a fall from occurring. To accomplish this, a phase-space driven controller will be used to collect gait metrics to determine a healthy gait pattern versus a fall. A phase space, in the context of gait, is a multivariate representation of parameters that assist gait pattern. The aim of this project was to analyze data collected from perturbation trials of both healthy and stroke-affected individuals to create the phase spaces that will be used to drive the controller of the smart AFO. Variables, such as ankle flexion, knee flexion, and trunk flexion were analyzed for nine healthy and seventeen stroke survivor subjects. This project required perturbation trial data be cleaned and relevant joint angles extracted. These joint angles were then used to develop a phase space using an algorithm, adjusted for every subject, perturbation level, trial, and parameter. Each of these phase spaces can then be compared to determine differences between parameters, healthy vs. stroke affected individuals, and fall vs. non fall.