MACHINE LEARNING AND VLSI BASED FREEZING OF GAIT DETECTION SYSTEM IN PARKINSON’S DISEASE

Ph.DDOCTOR OF PHILOSOPH

Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values.

BACKGROUND:Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. METHODS:87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. RESULTS:Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. CONCLUSION:Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements

Anthropometry measurements and demographic parameters of the 87 recruited subjects along with its corresponding distribution.

<p>Anthropometry measurements and demographic parameters of the 87 recruited subjects along with its corresponding distribution.</p

Coefficient of variation indicating the dispersion of data before and after normalization.

<p>Coefficient of variation indicating the dispersion of data before and after normalization.</p

Spearman correlation coefficients for the data before (raw) and after normalization.

<p>Significance level p < 0.05.</p

The criterion validity (agreement to 3D motion analysis system/a physiotherapist’s measurements, ICC(2,1)) and test-retest reliability among a subject’s trials (ICC(3,1)).

<p>The criterion validity (agreement to 3D motion analysis system/a physiotherapist’s measurements, ICC(2,1)) and test-retest reliability among a subject’s trials (ICC(3,1)).</p

Resulting multiple linear regression models for the mobility measures and gait parameters.

<p>The adjusted <i>R</i>² is shown along with the root-mean square error (RMSE) and the 10-fold cross-validated RMSE (CV-RMSE). All models and remaining independent variables are significant at p < 0.001. The variables selected are walking speed (<i>S</i>), height (<i>H</i>), age (<i>A</i>) and gender (<i>G</i>).</p