Grant: Research Tools and Instruments - Category 1 (<$150,000)

Walking, or gait, is a fundamental human motion. The loss or alteration of a person's walking skills greatly affects one's ability to function and quality of life. Biomechanists use state of the art motion capture systems to record gait patterns and describe the mechanics of normal and abnormal movements (gait analysis). Quantitative gait analyses typically examine kinematic, kinetic, and electromyographic data. The analysis of this mechanical and myoelectric data is challenging due to the high-dimensionality, temporal dependence, high variability, and highly correlated nature of the data. As a result, comparison of gait curves (i.e. normal vs. abnormal) is very difficult. To address the challenges associated with the large volumes of mechanical data, researchers have developed various data reduction and classification techniques. However, most are limited in either their ability to detect abnormal movement patterns and/or their interpretability. This study aims to further my work on the design and implementation of algorithms that reduce the vast quantities of mechanical data to a series of one-dimensional indices of normality based on age-matched normative data. The indices, or classifiers, use an automatic system (motion capture and subsequent data analysis) to detect movement abnormalities based on magnitude, pattern of motion, and correlations between gait curves. The output is a series of numerical scores that make mechanical and physiological sense and yield interpretable results. Very few classifiers exist that can satisfy the two-fold constraint of incorporating correlated multi-dimensional input data and providing readily interpretable output. The proposed work is novel in that it incorporates mechanical waveform data, multisegment foot kinematics, and myoelectric wavelet data. To date, no other classifiers include all of these important measures. The development of the classifier will provide researchers with an automated tool to reduce mechanical and myoelectric data and quantify differences in gait waveforms. The gait classifier will also identify which measures are the best discriminators of abnormal and normal motion. This will lead to a greater understanding of the differences in gait patterns between populations or individuals.