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Kun-Woo Park 3 Articles
Gait Analysis in Patients With Parkinson’s Disease: Relationship to Clinical Features and Freezing
Seong-Beom Koh, Kun-Woo Park, Dae-Hie Lee, Se Ju Kim, Joon-Shik Yoon
J Mov Disord. 2008;1(2):59-64.
DOI: https://doi.org/10.14802/jmd.08011
  • 17,609 View
  • 440 Download
  • 12 Crossref
AbstractAbstract PDF
Background:

The purpose of our study was to investigate gait dynamics and kinematics in patients with Parkinson’s disease (PD) and to correlate these features with the predominant clinical features and with the presence of the freezing of gait (FOG). We measured the temporospatial and kinematic parameters of gait in 30 patients with PD (M:F=12:18, age=68.43±7.54) using a computerized video motion analysis system.

Methods:

We divided the subjects into subgroups: (1) tremor-dominant (TD) group and postural instability and gait disturbance (PIGD) group and (2) FOG group and non-FOG group. We compared the gait parameters between the subgroups.

Results:

The walking velocity and stride length were reduced significantly in the PIGD group compared to the TD group. The PIGD group showed a significantly reduced range of motion in the pelvic and lower extremity joints by kinematics. Stride time variability was significantly increased and the pelvic oblique range was significantly reduced in the freezing gait disorder group.

Conclusion:

Our findings suggest that there are differences in the perturbation of the basal ganglia-cortical circuits based on major clinical features. The reduction of the pelvic oblique range of motion may be a compensatory mechanism for postural instability and contributes to stride time variability in patients with FOG.

Citations

Citations to this article as recorded by  
  • A machine learning model for prediction of sarcopenia in patients with Parkinson’s Disease
    Minkyeong Kim, Doeon Kim, Heeyoung Kang, Seongjin Park, Shinjune Kim, Jun-Il Yoo, Kyung-Wan Baek
    PLOS ONE.2024; 19(1): e0296282.     CrossRef
  • Machine learning approach for predicting state transitions via shank acceleration data during freezing of gait in Parkinson’s disease
    Ashima Khosla, Neelesh Kumar, Preeti Khera
    Biomedical Signal Processing and Control.2024; 92: 106053.     CrossRef
  • The gait parameters in patients with Parkinson’s Disease under STN-DBS therapy and associated clinical features
    Halil Onder, Ege Dinc, Kubra Yucesan, Selcuk Comoglu
    Neurological Research.2023; 45(8): 779.     CrossRef
  • Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization
    Robert Radu Ileșan, Claudia-Georgiana Cordoș, Laura-Ioana Mihăilă, Radu Fleșar, Ana-Sorina Popescu, Lăcrămioara Perju-Dumbravă, Paul Faragó
    Biosensors.2022; 12(4): 189.     CrossRef
  • Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: A Novel Deep One-Class Classifier
    Nader Naghavi, Eric Wade
    IEEE Journal of Biomedical and Health Informatics.2022; 26(4): 1726.     CrossRef
  • Development of Neuro-Degenerative Diseases’ Gait Classification Algorithm Using Convolutional Neural Network and Wavelet Coherence Spectrogram of Gait Synchronization
    Febryan Setiawan, An-Bang Liu, Che-Wei Lin
    IEEE Access.2022; 10: 38137.     CrossRef
  • Functional gait assessment in early and advanced Parkinson’s disease
    Hany Mohamed Eldeeb, Heba Samir Abdelraheem
    The Egyptian Journal of Neurology, Psychiatry and Neurosurgery.2021;[Epub]     CrossRef
  • Statistical methods for analysis of Parkinson’s disease gait pattern and classification
    Anup Nandy
    Multimedia Tools and Applications.2019; 78(14): 19697.     CrossRef
  • Prediction of Freezing of Gait in Parkinson’s Disease Using Statistical Inference and Lower–Limb Acceleration Data
    Nader Naghavi, Eric Wade
    IEEE Transactions on Neural Systems and Rehabilitation Engineering.2019; 27(5): 947.     CrossRef
  • Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem
    Nader Naghavi, Aaron Miller, Eric Wade
    Sensors.2019; 19(18): 3898.     CrossRef
  • Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A Survey
    Navleen Kour, Sunanda, Sakshi Arora
    IEEE Access.2019; 7: 156620.     CrossRef
  • A comparison of soft computing models for Parkinson’s disease diagnosis using voice and gait features
    Rekh Ram Janghel, Anupam Shukla, Chandra Prakash Rathore, Kshitiz Verma, Swati Rathore
    Network Modeling Analysis in Health Informatics and Bioinformatics.2017;[Epub]     CrossRef
Cruciform Pontine MRI Hyperintensities (“Hot Cross Bun” Sign) in Non-Multiple System Atrophy Patients
Seong-Beom Koh, Kun-Woo Park, Dae-Hie Lee
J Mov Disord. 2008;1(2):107-108.
DOI: https://doi.org/10.14802/jmd.08022
  • 10,145 View
  • 62 Download
  • 1 Crossref
PDF

Citations

Citations to this article as recorded by  
  • The “Hot Cross Bun Sign” in Spinocerebellar Ataxia Types 2 and 7–Case Reports and Review of Literature
    Ansuya Kasavelu Naidoo, Cait‐Lynn Deanne Wells, Yashvir Rugbeer, Neil Naidoo
    Movement Disorders Clinical Practice.2022; 9(8): 1105.     CrossRef
The Characteristics of Cognitive Impairment in Parkinson’s Disease and Recognition of Cognitive Symptom by Questionnaire
Hee Young Shin, Won Yong Lee, Kun-Woo Park
J Mov Disord. 2008;1(1):38-46.
DOI: https://doi.org/10.14802/jmd.08007
  • 8,294 View
  • 271 Download
AbstractAbstract PDF
Objective:

Parkinson’s disease (PD) is characterized by motor and non-motor symptoms including cognitive, autonomic, sleep, and sensory disturbances. Cognitive impairment may occur in up to 80% of PD patients, and dementia in approximately 30%. The purpose of this study is to evaluate the frequency of cognitive impairment and the characteristics of cognitive deficits and to know the possibility of early detection of cognitive deficits in outpatient clinics with the questionnaire for patients and caregivers.

Methods:

A total of 129 consecutive patients with idiopathic Parkinson’s disease were visited movement clinic from March 2006 to August 2006. Eighty-five patients performed cognitive test and questionnaires. All patients had motor symptoms with Hoehn and Yahr stage 0.5 to 3 (mean: 1.98±0.617), and evaluated with cognition by K-MMSE (Korean version of Mini-mental status examination), 7-MS (7-minutes screen test), and demographic features.

Results:

The frequency of cognitive impairment in PD patients was 44.7% (38/85), among them thirty (78.9%) patients complained memory disturbance. The characteristics of cognitive test were retrieval defect in memory, visuospatial dysfunction and categorical word fluency. With questionnaire, the complaint of memory decline and difficulties in activity of daily living (ADL) w ere important points of cognitive deficit in PD patients. However questionnaire did not showed significant correlation between complain of memory decline and cognitive deficit, only regular check with cognitive function test revealed the patient’s early cognitive impairment.

Conclusions:

The cognitive impairment was frequent in PD patients. The characteristics of cognitive testing w ere retrieval defect in memory function and frontal executive dysfunction.


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