Gait energy efficiency in children with cerebral palsy

Sarah Rosen, Carole A. Tucker, Samuel C.K. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Children with Cerebral Palsy (CP) expend up to three times the energy required for ambulation as compared to typically developed children of the same age. Measuring the metabolic energy required to execute a task is an intuitively appealing way to quantify task efficiency. Task energy demand is often quantified through pulmonary tests that measure oxygen consumption. Although providing an accepted measure of energy demand, these tests are technically demanding and staff intensive. For this reason, we sought a measure of gait efficiency based on spatiotemporal and kinematic parameters that would be reflective of the energy cost during ambulation in children with Cerebral Palsy. Gait data from 18 subjects with CP over 30 separate data collection sessions was used. Statistical analysis showed oxygen cost highly correlates to several kinematic variables, most notably, pelvic tilt, walking speed, landing angle and the biomechanical efficiency quotient (BEQ). The results of the work support the development of a computational model that would capture gait energy efficiency.

Original languageEnglish (US)
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages1220-1223
Number of pages4
DOIs
StatePublished - 2006
Externally publishedYes
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: Aug 30 2006Sep 3 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/TerritoryUnited States
CityNew York, NY
Period8/30/069/3/06

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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