Title :
Measuring gait symmetry in children with cerebral palsy using the SmartShoe
Author :
Ting Zhang ; Jiang Lu ; Uswatte, Gitendra ; Taub, Edward ; Sazonov, Edward S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA
Abstract :
Cerebral palsy (CP) is a group of non-progressive neuro-developmental conditions occurring in early childhood that causes movement disorders and physical disability. Many affected children have impaired function in movement and limitations in mobility. Measuring gait symmetry is essential in assessing clinical outcomes of rehabilitation. Modern sensor technology has made it possible to measure gait unobtrusively in the community. However, no wearable systems that allow for gait symmetry measurement in free living have been investigated for children with CP. In this study, data was collected from three children with CP by a wearable shoe sensor system (SmartShoe) in a community environment and the gait symmetry ratio was estimated from the sensor data prior and post rehabilitation therapy. The sensor data were processed by algorithms including data preprocessing, posture and activity classification, and calculation of symmetry ratio of stance. The gait symmetry metrics extracted by the automatic algorithms closely match the metrics manually estimated on the sensor data with an average mean absolute error of 1.235%), suggesting that the proposed method may be an effective way to evaluate rehabilitation progress in the community setting.
Keywords :
biomedical telemetry; body sensor networks; brain; data analysis; feature extraction; footwear; gait analysis; intelligent sensors; medical disorders; medical signal processing; neurophysiology; paediatrics; parameter estimation; patient rehabilitation; signal classification; symmetry; SmartShoe; activity classification; automatic algorithm; average mean absolute error; cerebral palsy; children gait symmetry measurement; clinical outcome assessment; community environment; data preprocessing algorithm; early childhood; gait symmetry metric extraction; gait symmetry ratio estimation; impaired movement function; manual metric estimation; mobility limitation; modern sensor technology; movement disorder; nonprogressive neurodevelopmental condition; physical disability; post rehabilitation therapy; posture classification; prior rehabilitation therapy; rehabilitation outcome assessment; rehabilitation progress evaluation; sensor data processing; stance symmetry ratio calculation; unobtrusive gait measurement; wearable shoe sensor system; Classification algorithms; Communities; Conferences; Footwear; Legged locomotion; Pediatrics;
Conference_Titel :
Healthcare Innovation Conference (HIC), 2014 IEEE
Conference_Location :
Seattle, WA
DOI :
10.1109/HIC.2014.7038871