ar gait training for parkinson's disease

Conditions: PD - Parkinson's Disease

Summary: gait disorders in parkinson's disease (pd), particularly in complex environments or under stress, present challenges for accurate evaluation and classification, such as in cases of freezing of gait. traditional clinical and laboratory settings often fail to replicate the complexity needed for precise classification, making effective rehabilitation difficult. this study aims to address these challenges by developing an augmented reality (ar)-based environment that mimics real-world stressors and dynamically adapts to the patient's condition. the ar system is designed to facilitate individualized gait training and rehabilitation by modifying environmental difficulty based on real-time feedback from gait performance and stress levels. building on gentile's taxonomy of tasks, the investigators have incorporated pd-specific factors, such as cognitive dual tasks, into our environment classification system. preliminary results suggest that this system effectively elicits varying gait and heart rate variability (hrv) responses, indicating different stress levels. this trial will further test the ar environment's ability to classify patients based on their responses to complex, interactive environments, while also investigating the effects of adaptive ar-based gait training on both gait and stress management in individuals with pd.

gait disorder in parkinson's disease (pd), has been reported to be related to complex environments and stress situations. to perform precise evaluation on gait disorder, such as freezing of gait, is not easy in clinical and/or lab environment, causing the difficulty to make accurate classification of gait disorder. designing a standard environment stimulation paradigm helps to promote precise classification of gait disorders. monitoring the stress situation that causes gait disorder, and dynamically modifying environment assists in the facilitation of effective individualized gait rehabilitation for pd patients. although gentile's taxonomy of tasks provides a structure to examine the complexity of movement task in accordance with environmental context, this taxonomy and environmental context does not specifically reflects pd movement problems. in this on-going project, the investigators developed an environment classification system based on gentile's taxonomy, but added pd related factors, such as cognitive dual task to meet the needs of this population. our preliminary results showed that this novel classification successfully induced different levels of gait and hrv responses, suggesting different stress situations. for the following, the investigators plan to apply the augment reality (ar) technique to produce standard interactive testing and training environment. ar allows us to mimic complex environments in lab for evaluation and rehabilitation. in addition, ar could interactively change the difficulty of environment according to the patients'gait and stress situation which is important for developing effective personal rehabilitation program the purposes for this project are as follows: 1. test the ar environment stimulation on classifying patients and establish the relationships among challenging ar environment, pressure, and gait 2. investigate the effects of interactive ar gait training on gait and stress for individuals with pd.

Eligibility criteria:
inclusion criteria: - clinical diagnosis of parkinson disease. exclusion criteria: * musculoskeletal injuries on legs * osteoporosis. * any peripheral or central nervous system injury or disease patients.

Keywords: augmented reality gait taining heart rate variability (hrv)