Pilot Study to Enhance the Repeatability, Validity and Reliability of Traditional Observational Falls Risk Assessments by Incorporating Markerless Motion Capture Technology

• Author / Creator

  Osuji, Emmanuella

• Certain individuals, especially those in the geriatric population experience falls each year. Although there are validated assessment tools, like the Berg Balance Scale (BBS), they can be very subjective and insensitive to change. These tools are also limited to face-to-face interactions between clinicians and patients. During the global COVID-19 pandemic, where face-to-face interactions have become difficult, especially for patients living in rural areas, a better way to assess the falls risk of individuals becomes pertinent.
  Kinetisense is a markerless motion capture technology that has the ability to assess patients and produce objective results from these assessments.
  The purpose of this pilot study was to develop a research protocol, using tasks from the BBS, to assess participants at risk of falling using markerless motion capture. The BBS assessments using markerless motion capture are referred to in this thesis as enhanced Virtual Berg Balance Scale (eV-BBS).
  The long-term goal of this research is to validate markerless motion capture to objectively carry out risk assessments for falling and transform them into objective measures. Doing so would improve accessibility for patients living in rural areas, as well as establish a better way to store patient information to be accessible to clinicians over time.
  This was a test-retest reliability study. It explored the hypotheses that there were no differences in the validity, reliability and repeatability of the eV-BBS tasks compared to BBS.
  A convenience sample of ten participants from the healthy population and 4 participants from the long-term care (LTC) population were observed as they performed the BBS and eV-BBS tasks. The LTC population was divided into fallers and non-fallers. Following the rules of the BBS, no instructions were given to the participants on how best to perform each task outside of the time it took to complete each task which was stipulated by the researcher. While all the tasks of the BBS were used in this study, a select number of tasks were used in the eV-BBS protocol. This is because the excluded tasks were too complex for the markerless motion capture to analyse reliably at its current stage of development.
  The validity (comparing the scores of the two assessment systems) of this study could not be analysed due to the small sample of individuals from the LTC population. Instead, a comparison of groups between the healthy participants and LTC participants was carried out to show the degree of variability between the healthy and LTC participants. This comparison showed a substantial difference in the values obtained between participants in their respective groups. This was attributed to the fact that each participant carried out the task differently and markerless motion capture was sensitive enough to detect the differences between participants. While the healthy population repeated each task 5 times, the LTC population only repeated each task once. As such, the test-retest reliability and repeatability analysis could only be conducted on the healthy population data but not the LTC population data.
  The eV-BBS had two main subsets: Balance tasks which consists of static positions and Functional tasks which consists of dynamic positions. For the healthy population, the study showed that the balance tasks, was able to detect differences between individuals while showing good consistency within individuals (Intraclass Correlation Coefficient (ICC) ranged from 0.81 to 0.99). Using an alternative measure of repeatability (coefficient of variation), the study confirmed that the eV-BBS was repeatable (Coefficient of Variation (COV) <20%) in the healthy group. The ICC for the functional tasks indicates poor reliability with ICC values in the range between 0.1 and 0.60. Closer examination looking at the within-participant (i.e., COV), in these functional tasks/dynamic balance tests, indicates that a significant proportion of the variation is due to the fact that an individual does not perform these tasks consistently. However, we cannot definitively state that the observed variation between individuals in the functional tasks is due to technique rather than noise in the markerless motion capture data and the analysis algorithms that were used to extract features from the Markerless motion capture data may require further refinement.
  In conclusion, this pilot study shows the capabilities of implementing markerless motion capture systems as part of remote falls risk assessments Improvements to the research protocol and data analysis were identified.

• Subjects / Keywords

  • Markerless Motion Capture Technology
  • Kinetisense

• Graduation date

  Spring 2022

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-0fpw-5c38

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.