Student: Vinisha Venugopal
Project Mentors: Dr. Narayanan Krishnamurthi – CONHI
Dr. James Abbas- SBHSE
Dr. Claire Honeycutt- SBHSE
YouTube Link: View the video link below before joining the zoom meeting
Zoom Link: https://asu.zoom.us/j/99123714679
Zoom meeting time: 9am - 11am
Abstract
Progressive gait disorder in Parkinson’s Disease (PD) is usually exhibited as reduced step length, stride length and gait speed, as well as shuffling of gait, postural imbalance, spatial and temporal gait asymmetries. Many studies have confirmed that people with PD can improve walking patterns when external cues are provided. Due to impaired proprioception, people with PD are not aware if they are following the cues correctly. To address this, in our prior work real-time feedback of step length and back angle were provided to improve gait and posture. To investigate if these acute benefits of real-time feedback could be sustained, we conducted a long-term (3 sessions/week, for six consecutive weeks) treadmill-based real-time feedback training (RTFT) study. When using standard data processing techniques, there were errors in the identification of the timings of heel-strike and toe-off, resulting in an inaccurate calculation of step length. Some of these errors were due to brief periods of missing data, which mostly occurred during the swing phase, and others were due to spurious peaks and troughs in the data that did not correspond to a heel-strike or toe-off event. The aim of this study is to implement a new algorithm to process the position signals from sensors on the feet to improve detection of heel-strike or toe-off. The time-series data set from each sensor was smoothed to remove spurious peaks and troughs using a window size that was larger than the maximum number of consecutive missing data points, that aids in accurate detection of peaks and troughs. Detection of successive threshold crossings was restricted to be within 500-1000ms (based on typical walking in which successive heel-strike and toe-off occur in that interval). The resultant signals were used for subsequent calculation of more accurate spatiotemporal parameters such as step length, step time, step length asymmetry, and step time asymmetry.