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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

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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.

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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

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.

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Authors and Affiliations

  1. Department of Physiotherapy, Singapore General Hospital, National Heart Centre Level 7, 5 Hospital Drive, Singapore, 169609, Republic of Singapore

    Dawn Tan, Yong-Hao Pua, Shaminian Balakrishnan & Aileen Scully

  2. Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Republic of Singapore

    Dawn Tan, Kumar Manharlal Prakash & Eng-King Tan

  3. School of Health and Sport Sciences, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia

    Kelly J. Bower & Ross A. Clark

  4. National Neuroscience Institute, Singapore General Hospital, Outram Road, Singapore, 169608, Republic of Singapore

    Kumar Manharlal Prakash & Eng-King Tan

  5. Division of Nursing, Singapore General Hospital, Outram Road, Singapore, 169608, Republic of Singapore

    Jing-Si Chew, Evelyn Poh & Siok-Bee Tan

Authors
  1. Dawn Tan
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  2. Yong-Hao Pua
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  3. Shaminian Balakrishnan
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  4. Aileen Scully
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  5. Kelly J. Bower
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  6. Kumar Manharlal Prakash
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  7. Eng-King Tan
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  8. Jing-Si Chew
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  9. Evelyn Poh
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  10. Siok-Bee Tan
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  11. Ross A. Clark
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Correspondence to Dawn Tan.

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Tan, D., Pua, YH., Balakrishnan, S. et al. 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. Med Biol Eng Comput 57, 369–377 (2019). https://doi.org/10.1007/s11517-018-1868-2

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  • DOI: https://doi.org/10.1007/s11517-018-1868-2

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