Motion Analysis in Neurological Rehabilitation: From the Lab to the Clinic

MCML Authors

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

Human motion analysis and biomechanics are fundamental in a clinical environment, and together, they provide relevant and precise information towards diagnosing numerous neurodegenerative conditions such as stroke, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, etc. In most neurological disorders, walking is commonly impacted, where performance, quantity, and quality are affected. Thus, motion analysis aims at understanding the cause of altered motion patterns, mainly assisting with the prevention, identification, and rehabilitation. Usually, motion analysis assessment relies on the patient’s self-report and the practitioner’s visually assessed observations. Therefore, such assessments are often subjective and susceptible to human-induced error. In contrast, sophisticated devices can provide quantitative accuracy by equipping practitioners with precise, reliable, and objective measurements to simultaneously monitor an extensive set of parameters for gait analysis (e.g., 3D joint kinematics, muscle activation patterns, muscle forces, and coordination patterns). This book chapter addresses the challenges and describes the technological solutions considered when moving out of the lab condition to the real-world environments, in this case, the clinical setting.

article

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Translational Neurorehabilitation

Aug. 2024.

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Authors

R. Baptista • B. Liew • S. Pizzocaro • X. Zhai • S. Galasso • D. Rügamer • T. Waterkeyn • I. Boukhennoufa • X. Zhu • A. M. Nunzio

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability