Quantification of Disease Induced Motion Impairment using simulation techniques.

  • Abdul Aziz Vaqar A. Hulleck

Student thesis: Master's Thesis

Abstract

Gait is defined as a person's walking pattern, and understanding it is critical as a quantitative diagnostic, prognostic, and rehabilitation tool for various neuromusculoskeletal disorders, degenerative diseases, aging and beyond. Analysis of data obtained during motion capture to assess gait disorders needs to be systematic and reliable. Impaired gait is typically neither as cyclic nor symmetric as healthy gait making identification of motion impairment arduous. In general, gait is characterized by spatiotemporal, kinematic, and kinetic parameters. Gait data obtained in the process needs to be managed, analyzed, and made available in a format easily usable by the clinical team. Walking impairment is a hallmark of many musculoskeletal and neurological diseases and accurate reliable quantification of gait characteristics at a given time, and importantly, monitoring and evaluating them over time, enables proper diagnosis and management. Gait analysis is highly relevant in rehabilitation, treatment planning, outcome assessment, and pre-operative planning for various health challenges including cerebral palsy, multiple sclerosis, Parkinson's disease, and stroke. In joint arthroplasty, clinical gait data can be used to evaluate patient's progress before and after surgery. The thesis aims at quantifying motion impairment due to ageing in healthy subjects with no reported co-morbidities and compare it with younger subjects' motion based on joint kinetics. Clinical hip osteoarthritis patient evaluation is based on questionnaires which are subjective. Previous studies evaluate gait disorder using kinematic and limited kinetic parameters obtained from force plate. Additionally, gait kinetics for hip osteoarthritis patients is compared with age matched healthy subjects using inverse dynamic analysis and motion capture. To quantify kinetic deviations from normal gait the GDI-Kinetic index was modified to account for joint forces, and moments. The index can be used to compare a subjects' gait from the baseline healthy population and provide the distance from this population in terms of the standard deviation. A clear distinction between healthy and impaired gait is reported with 1 of 5 healthy aged subjects on right side and 4 of 5 subject on left side exhibiting abnormal gait score as compared to young subjects. The deviation in joint kinetics for hip osteoarthritis has also been identified using the index hence providing a quantitative analysis and reducing the data for simpler clinical evaluation of gait.
Date of AwardJun 2021
Original languageAmerican English

Keywords

  • Gait Assessment
  • Musculoskeletal modeling
  • Inverse dynamic Analysis
  • Motion capture
  • GDI-Kinetic Index.

Cite this

'