1.306 GAIT ANALYSIS IN PARKINSON'S DISEASE PATIENTS WITH MOTOR FLUCTUATIONS IN DIFFERENT WALKWAYS: GAITRITE SYSTEM

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

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Effects of Onabotulinum Toxin A on Gait in Parkinson’s Disease Patients with Foot Dystonia

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2021.42 ◽

2021 ◽

pp. 1-6

Author(s):

Pei Huang ◽

Yuan-Yuan Li ◽

Jung E. Park ◽

Ping Huang ◽

Qin Xiao ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Botulinum Toxin ◽

Gait Analysis ◽

Rating Scale ◽

Analog Scale ◽

Toxin A ◽

Timed Up And Go ◽

Balance Test ◽

Onabotulinum Toxin A

ABSTRACT: We investigated the effects of botulinum toxin on gait in Parkinson’s disease (PD) patients with foot dystonia. Six patients underwent onabotulinum toxin A injection and were assessed by Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), visual analog scale (VAS) of pain, Timed Up and Go (TUG), Berg Balance Test (BBT), and 3D gait analysis at baseline, 1 month, and 3 months. BFMDRS (p = 0.002), VAS (p = 0.024), TUG (p = 0.028), and BBT (p = 0.034) were improved. Foot pressures at Toe 1 (p = 0.028) and Midfoot (p = 0.018) were reduced, indicating botulinum toxin’s effects in alleviating the dystonia severity and pain and improving foot pressures during walking in PD.

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To assess whether a “virtual admission” can be useful for Parkinson’s disease patients with severe motor fluctuations

Neurological Sciences ◽

10.1007/s10072-020-04939-9 ◽

2021 ◽

Author(s):

Anna Randall ◽

J. Panicker ◽

A. Macerollo ◽

S. H. Alusi

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Motor Fluctuations ◽

Severe Motor

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Gait analysis for early Parkinson’s disease detection based on deep learning

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0003 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-12

Author(s):

Jyothsna Kondragunta ◽

Christian Wiede ◽

Gangolf Hirtz

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Gait Analysis ◽

Pose Estimation ◽

Early Identification ◽

Human Gait ◽

3D Pose Estimation ◽

Wearable Technologies ◽

Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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