Offaxis neuromuscular training of knee injuries using an offaxis robotic elliptical trainer

Song Joo Lee, Yupeng Ren, François Geiger, Alison Hsin-I Chang, Joel M. Press, Li Qun Zhang*

*Corresponding author for this work

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

11 Scopus citations

Abstract

The goal of this study was to use an offaxis robotic elliptical trainer to improve off-axis neuromuscular control in people with knee injuries. Thirteen individuals with knee injuries participated in the study. Among them, 8 individuals participated in 18 sessions of pivoting offaxis intensity-adjustable neuromuscular control training (POINT) (3 sessions/week for 6 weeks including 3 evaluation sessions) to improve offaxis neuromuscular control, specifically dynamic lower limb stability in pivoting. 5 individuals served as controls who only participated in the three evaluations. Following POINT patients in the training group reduced pivoting instability (p0.024), while the control group did not (p0.118). Individuals in the training group were able to hop farther in a single leg hop for distance task, take shorter in 12m hop time for time task, and reported reduced knee pain. The results suggest that subject-specific POINT utilizing the novel robotic elliptical trainer can be implemented as a rehabilitation protocol for patients with knee injuries to improve their lower limb functions and reduce knee symptoms.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages2081-2084
Number of pages4
DOIs
StatePublished - Dec 26 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

ASJC Scopus subject areas

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

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