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Medical & Biological Engineering & Computing

, Volume 57, Issue 2, pp 369–377 | Cite as

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 TanEmail author
  • Yong-Hao Pua
  • Shaminian Balakrishnan
  • Aileen Scully
  • Kelly J. Bower
  • Kumar Manharlal Prakash
  • Eng-King Tan
  • Jing-Si Chew
  • Evelyn Poh
  • Siok-Bee Tan
  • Ross A. Clark
Original Article
  • 232 Downloads

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

Keywords

Parkinson’s disease Assessment Gait Instrumentation Kinect 

Notes

Acknowledgements

The authors would like to thank the Department of Nursing and Department of Physiotherapy at Singapore General Hospital for their support and assistance with data collection.

Funding information

Author RAC is funded by a National Health and Medical Research Council Career Development Fellowship (#1090415). Author EKT is funded by a National Medical Research Council, Singapore Translational Research (STaR) Investigator award.

Compliance with ethical standards

Conflict of interest

The authors declare they have no conflict of interest.

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Dawn Tan
    • 1
    • 2
    Email author
  • Yong-Hao Pua
    • 1
  • Shaminian Balakrishnan
    • 1
  • Aileen Scully
    • 1
  • Kelly J. Bower
    • 3
  • Kumar Manharlal Prakash
    • 2
    • 4
  • Eng-King Tan
    • 2
    • 4
  • Jing-Si Chew
    • 5
  • Evelyn Poh
    • 5
  • Siok-Bee Tan
    • 5
  • Ross A. Clark
    • 3
  1. 1.Department of PhysiotherapySingapore General HospitalSingaporeRepublic of Singapore
  2. 2.Duke-NUS Graduate Medical SchoolSingaporeRepublic of Singapore
  3. 3.School of Health and Sport SciencesUniversity of the Sunshine CoastSippy DownsAustralia
  4. 4.National Neuroscience InstituteSingapore General HospitalSingaporeRepublic of Singapore
  5. 5.Division of NursingSingapore General HospitalSingaporeRepublic of Singapore

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