title: Automatic detection of temporal gait parameters in poststroke individuals.
abstract: Approximately one-third of people who recover from a stroke require some form of assistance to walk. Repetitive task-oriented rehabilitation interventions have been shown to improve motor control and function in people with stroke. Our long-term goal is to design and test an intensive task-oriented intervention that will utilize the two primary components of constrained-induced movement therapy: massed, task-oriented training and behavioral methods to increase use of the affected limb in the real world. The technological component of the intervention is based on a wearable footwear-based sensor system that monitors relative activity levels, functional utilization, and gait parameters of affected and unaffected lower extremities. The purpose of this study is to describe a methodology to automatically identify temporal gait parameters of poststroke individuals to be used in assessment of functional utilization of the affected lower extremity as a part of behavior enhancing feedback. An algorithm accounting for intersubject variability is capable of achieving estimation error in the range of 2.6-18.6% producing comparable results for healthy and poststroke subjects. The proposed methodology is based on inexpensive and user-friendly technology that will enable research and clinical applications for rehabilitation of people who have experienced a stroke.
authors.affiliation: Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487-0286, USA. plopezmeyer@bama.ua.edu
authors.forename: Paulo
authors.initials: P
authors.lastname: Lopez-Meyer
authors.forename: George D
authors.initials: GD
authors.lastname: Fulk
authors.forename: Edward S
authors.initials: ES
authors.lastname: Sazonov
mesh_terms: D000054:Acceleration; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D003020:Clothing; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012775:Shoes; D012815:Signal Processing, Computer-Assisted; D000071939:Stroke Rehabilitation