Literature DB >> 30123947

Automated analysis of gait and modified timed up and go using the Microsoft Kinect in people with Parkinson's disease: associations with physical outcome measures.

Dawn Tan1,2, Yong-Hao Pua3, Shaminian Balakrishnan3, Aileen Scully3, Kelly J Bower4, Kumar Manharlal Prakash5,6, Eng-King Tan5,6, Jing-Si Chew7, Evelyn Poh7, Siok-Bee Tan7, Ross A Clark4.   

Abstract

Instrumenting physical assessments in people with Parkinson's disease can provide valuable and sensitive information. This study aimed to investigate whether variables derived from a Kinect-based system can provide incremental value over standard habitual gait speed (HGS) and timed up and go (TUG) variables by evaluating associations with (1) motor and (2) postural instability and gait difficulty (PIGD) subscales of the Unified Parkinson's Disease Rating Scale (UPDRS). Sixty-two individuals with Parkinson's disease (age 66 ± 7 years; 74% male) undertook an instrumented HGS and modified TUG tests, in addition to the UPDRS. Multivariable regression models were used to evaluate the associations of the Kinect measures with UPDRS motor and PIGD scores. First step length during the TUG and average step length and vertical pelvic displacement during the HGS were significantly associated with the PIGD subscale (P < 0.05). The only Kinect-derived variable showing additive benefits over the standard measures for the PIGD association was HGS vertical pelvic displacement. The only standard or Kinect-derived variable significantly associated with the motor subscale was first step length during the TUG (P < 0.01). This study provides preliminary evidence to support the use of a low-cost, non-invasive method of instrumenting gait and TUG tests in people with Parkinson's disease. Graphical abstract ᅟ.

Entities:  

Keywords:  Assessment; Gait; Instrumentation; Kinect; Parkinson’s disease

Mesh:

Year:  2018        PMID: 30123947     DOI: 10.1007/s11517-018-1868-2

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  5 in total

1.  Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.

Authors:  Zhuoyu Zhang; Ronghua Hong; Ao Lin; Xiaoyun Su; Yue Jin; Yichen Gao; Kangwen Peng; Yudi Li; Tianyu Zhang; Hongping Zhi; Qiang Guan; LingJing Jin
Journal:  J Neuroeng Rehabil       Date:  2021-12-04       Impact factor: 4.262

Review 2.  Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review.

Authors:  Serena Cerfoglio; Claudia Ferraris; Luca Vismara; Gianluca Amprimo; Lorenzo Priano; Giuseppe Pettiti; Manuela Galli; Alessandro Mauro; Veronica Cimolin
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

3.  A summary index derived from Kinect to evaluate postural abnormalities severity in Parkinson's Disease patients.

Authors:  Ronghua Hong; Tianyu Zhang; Zhuoyu Zhang; Zhuang Wu; Ao Lin; Xiaoyun Su; Yue Jin; Yichen Gao; Kangwen Peng; Lixi Li; Lizhen Pan; Hongping Zhi; Qiang Guan; Lingjing Jin
Journal:  NPJ Parkinsons Dis       Date:  2022-08-02

4.  Validation, Reliability, and Responsiveness Outcomes Of Kinematic Assessment With An RGB-D Camera To Analyze Movement In Subacute And Chronic Low Back Pain.

Authors:  Manuel Trinidad-Fernández; David Beckwée; Antonio Cuesta-Vargas; Manuel González-Sánchez; Francisco-Angel Moreno; Javier González-Jiménez; Erika Joos; Peter Vaes
Journal:  Sensors (Basel)       Date:  2020-01-27       Impact factor: 3.576

5.  Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System.

Authors:  Guillermo Díaz-San Martín; Luis Reyes-González; Sergio Sainz-Ruiz; Luis Rodríguez-Cobo; José M López-Higuera
Journal:  Sensors (Basel)       Date:  2021-03-09       Impact factor: 3.576

  5 in total

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