# QUANTITATIVE GAIT ANALYSIS

> **NIH NIH R01** · NEW YORK STATE DEPARTMENT OF HEALTH · 1987 · $64,248

## Abstract

Quantitative gait analysis using computer aided videomotion analysis, force
plates and electromyography is of recognized value in assessment of gait
disabilities and in quantitative evaluation of treatment.  But, despite
dramatic improvements in techniques, gait analysis still lacks widespread
clinical utility because of uncertainties relating to data selection,
manipulation, and analysis.  Since data acquisition is usually limited to a
single test session over several gait cycles, one vital problem concerns
selection of a gait cycle for analysis that is representative of the
patient's gait and determination of its reliability as a basis for clinical
decision making.  Another problem relates to the manipulation of large
quantities of data generated by measurements of various kinetic, kinematic
and electromyographic parameters over several gait cycles to detect
clinically significant patterns of performance.  This process is further
complicated by the fact that gait patterns vary among patients with the
same syndorme and to a certain extent, vary even among normals.
Interpretration may be simplified by using statistical pattern recognition
techniques, but for a pattern recognition approach to be successful, the
enormous quantity of data must be reduced to a parsimonious set of features
which describe gait patterns accurately.  Furthermore, representation of
graphic patterns associated with various gait parameters in terms of a
discrete set of variables would make numerical comparison more meaningful.

The first part of this study will investigate repeatability and clinical
reliability of selected kinetic, kinematic and electromyographic parameters
obtained from repeated gait analyses on normal subjects and two
representative groups of orthopaedic patients, using a statistical
criterion (variance ratio).  The feasibility of using the variance ratio as
a criterion for averaging gait cycles to yield a representative cycle also
will be examined.  The second part of the study will deal with the
application of principal component analysis for representing gait patterns
in a concise manner, as an initial step to application of pattern
recognition techniques.  The effectiveness of the derived features in
representing gait abnormalities will be evaluated using the same normal and
orthopaedic patient subjects.  It is anticipated that the results of the
proposed study will enhance the clinical effectiveness of quantitative gait
analysis by defining practical computer algorithms for selecting and
assessing reliability of raw gait data, and by defining techniques for
feature selection that can be used effectively in pattern recognition
techniques.

## Key facts

- **NIH application ID:** 3156996
- **Project number:** 5R01AR034886-03
- **Recipient organization:** NEW YORK STATE DEPARTMENT OF HEALTH
- **SAM.gov UEI:** F863WQVMZSK7
- **Principal Investigator:** MURALI KADABA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 1987
- **Award amount:** $64,248
- **Award type:** 5
- **Project period:** 1985-01-01 → 1989-01-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/3156996

## Citation

> US National Institutes of Health, RePORTER application 3156996, QUANTITATIVE GAIT ANALYSIS (5R01AR034886-03). Retrieved via MORVS 2026-10-24 from https://api.morvs.ai/grant/nih/3156996. Licensed CC0.

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