Abstract
Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand the capacities and considerations for implementing these types of systems. The current review provides a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the successful adoption of markerless motion capture technology for the assessment of lower-limb musculoskeletal kinematics in sport medicine and performance settings. 31 articles met the a priori inclusion criteria of this analysis. Findings from the analysis indicate that the improving accuracy of these systems via the refinement of machine learning algorithms, combined with their cost efficacy and the enhanced ecological validity outweighs the current weaknesses and threats. Further, the analysis makes clear that there is a need for multidisciplinary collaboration between sport scientists and computer vision scientists to develop accurate clinical and research applications that are specific to sport. While work remains to be done for broad application, markerless motion capture technology is currently on a positive trajectory and the data from this analysis provide an efficient roadmap toward widespread adoption.
Highlights
Markerless motion capture systems have emerged as a promising tool to assess movement in both research and clinical settings
The success of several variants of system configurations and the encouraging initial results as well as clinical applications serve as the foundation for the future of biomechanically accurate markerless motion capture
With thoughtful system design grounded in multidisciplinary collaborations, markerless motion capture will develop accurate clinical and research applications to expand current motion capture capabilities as well as its reach
Summary
Markerless motion capture systems have emerged as a promising tool to assess movement in both research and clinical settings. To ensure a systematic review of the literature was conducted to build the foundation of the SWOT analysis, the following electronic databases were searched for relevant studies from their inception through February 2021, and a second time through December 2021: PubMed, ProQuest Health & Medical Collection, CINAHL, and Google Scholar. These electronic databases were searched using combinations of key words related to the scope of the review (search terms: lower extremity, kinematics, ankle, knee, hip, pelvis, markerless motion capture) and Boolean operators OR and AND were used to combine search terms. The highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006, 2007, 2008, 2010; Mündermann et al, 2006b, 2007)
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