Studying freezing of gait (FOG) in the lab has proven problematic. This has primarily been due to the difficulty in designing experimental setups that maintain high levels of ecological validity whilst also permitting sufficient levels of experimental control. To help overcome these challenges, we have developed a virtual reality (VR) environment with virtual doorways, a situation known to illicit FOG in real life. To examine the validity of this VR environment, an experiment was conducted, and the results were compared to a previous "real-world" experiment. A group of healthy controls ( = 10) and a group of idiopathic Parkinson disease (PD) patients without any FOG episodes ( = 6) and with a history of freezing (PD-f, = 4) walked under three different virtual conditions (no door, narrow doorway (100% of shoulder width) and standard doorway (125% of shoulder width)). The results were similar to those obtained in the real-world setting. Virtual doorways reduced step length and velocity while increasing general gait variability. The PD-f group always walked slower, with a smaller step length, and showed the largest increases in gait variability. The narrow doorway induced FOG in 66% of the trials, while the standard doorway caused FOG in 29% of the trials. Our results closely mirrored those obtained with real doors. In short, this methodology provides a safe, personalized yet adequately controlled means to examine FOG in Parkinson's patients, along with possible interventions.
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http://dx.doi.org/10.1155/2018/2957427 | DOI Listing |
Parkinsonism Relat Disord
January 2025
Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand; The Academy of Science, The Royal Society of Thailand, Bangkok, 10330, Thailand. Electronic address:
Introduction: Detecting Freezing of Gait (FOG) poses challenges, with the subjective 6-item FOG Questionnaire relying solely on patient perception. We aim to create a holistic FOG Detection Toolkit combining subjective and objective elements (descriptions, images, and videos) to improve FOG detection precision.
Methods: Development of the FOG Detection Toolkit involved a detailed cover sheet on FOG and its triggers, along with video exemplars and a 4-item FOG-specific self-assessment questionnaire, all rigorously validated.
Sensors (Basel)
December 2024
Department of Neurology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group's pFOG algorithm, provide objective data to better understand this phenomenon.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objectives: Freezing of Gait (FOG) is one of the disabling symptoms in patients with Parkinson's Disease (PD). While it is difficult to early detect because of the sporadic occurrence of initial freezing events. Whether the characteristic of gait impairments in PD patients with FOG during the 'interictal' period is different from that in non-FOG patients is still unclear.
View Article and Find Full Text PDFNeurorehabil Neural Repair
January 2025
Institute for Health and Sport (IHeS), Victoria University, Melbourne, VIC, Australia.
Non-invasive brain stimulation (NIBS) is sometimes used alongside medication to alleviate motor symptoms in people with Parkinson's disease (PD). However, the evidence supporting NIBS's effectiveness for improving motor function in PD patients is uncertain. .
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson's disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 ( = 11) was collected in a previous study.
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