Modeling and Assessing Hip-Knee Joint Coordination Based on Cyclograms Using a Portable Motion Capture System

Ningcun Xu, Chen Wang, Liang Peng*, Xiwei Peng, Zeng Guang Hou*, Pu Zhang, Zejia He, Jingyao Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Cyclograms are useful for describing interjoint coordination in clinical gait analyses. In this paper, we establish a model for generating hip-knee cyclograms at different walking speeds by using B-Spline curves, which could provide reference standards for evaluating hip-knee joint coordination. We use the shape similarity between individual and reference cyclograms to develop a hip-knee joint coordination assessment metric: the dynamic time warping-shape context (DTW-SC). The DTW algorithm is used to match corresponding points between the hip-knee cyclograms, and the matched results are visualized to show abnormal changes in gait patterns. The distance between corresponding points is determined through shape context descriptors and applied to quantify the hip-knee joint coordination of the subjects. To verify the effectiveness of the proposed approach, a stereo vision-based portable lower limb motion capture system is developed to collect hip and knee joint angle data from five healthy and five hemiplegic subjects walking at comfortable speeds. Experimental results show that the range of the modeling error is [0.84°, 2.76°]. DTW-SC metric can visualize and quantify the abnormal patterns of the subject's hip-knee joint coordination. The work is meaningful in evaluating the hip-knee joint coordination of hemiplegic patients.

Original languageEnglish
Pages (from-to)245-256
Number of pages12
JournalIEEE Transactions on Medical Robotics and Bionics
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Feb 2024
Externally publishedYes

Keywords

  • B-spline curve
  • Hip-knee cyclogram
  • dynamic time warping
  • joint coordination evaluation metric
  • motion capture
  • shape context descriptor
  • stereo vision

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