Evaluating Gait Patterns in healthy individuals and populations with neurological conditions
The gait pattern, the individual way in which we walk, is unique to each person and will change with age, injury, or illness. These gait patterns contain valid insights for rehabilitation after neurological conditions, such as a spinal cord injury, stroke, or Parkinson's disease.
Our research examines how neurological conditions and age alter gait patterns and identifies factors influencing these changes.
With this project, we disentangle the different characteristics of gait patterns in healthy, elderly, and neurologically impaired persons in real-world conditions. Our project concentrates on persons affected by a spinal cord injury, stroke, or Parkinson’s disease.
Everyday situations mimicked in a controlled lab environment – walking at various inclinations, at different speeds, and under dual-tasking conditions – will enhance our comprehension of gait pattern adaptations to various environmental conditions. Both internal factors, such as physical attributes and health status, and external factors, such as terrain or distractions, influence a person's gait pattern.
We utilize unobtrusive measurement systems to translate these lab-observations into the real-world setting. Wearable sensors will collect information on ambulatory and cardiac function over several days in the person’s everyday environment. We will apply machine learning algorithms on the collected data to quantify the ambulatory activity and characterize the individual gait pattern. A population-specific prediction model integrating the person’s characteristics, results from standard clinical assessments, and musculoskeletal factors will be developed to forecast gait patterns.
This comprehensive approach will enhance our understanding of neurologically impaired gait in various conditions and potentially pave the way for informed personalized rehabilitation strategies.