Functional modeling of pedaling kinematics for the Stroke patients. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Chakraborty, Sounak; Dey, Tanujit; Mukherjee, Anish; Alberts, Jay L.; Linder, Susan M.
• Is Part Of: Journal of biopharmaceutical statistics. Volume 30:Issue 4(2020)
• Page Start: 674
• Page End: 688
• Publication Date: 2020-07-03
• Subjects: Bayesian bootstrap -- Fréchet distance -- functional ANOVA -- personalized treatment -- nonparametric tests; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2020.1730872
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: Understanding deficits in motor control through the analysis of pedaling biomechanics plays a key role in the treatment of stroke patients. A thorough study of the impact of different exercise patterns and workloads on the change between pre- and post-treatment movement patterns in the patients is therefore of utmost importance to the clinicians. The objective of this study was to analyze the difference between pre- and post-treatment pedaling torques when the patients are subject to different exercise groups with varying workloads. The effects of affected vs unaffected side along with the covariates age and BMI have also been accounted for in this work. Two different three-way ANOVA-based approaches have been implemented here. In the first approach, a random projection-based ANOVA technique has been performed treating the pedaling torques as functional response, whereas the second approach utilizes distance measures to summarize the difference between pre- and post-treatment torques and perform nonparametric tests on it. Bayesian bootstrap has been used here to perform tests on the median distance. A group of stroke patients have been studied in the Cleveland Clinic categorizing them into different exercise groups and workload patterns. The data obtained have been analyzed with the aforementioned techniques, and the results have been reported here. These techniques turn out to be promising and will help clinicians recommend personalized treatment to stroke patients for optimal results.