Posters
Presenting Author Academic/Professional Position
Medical Student
Academic Level (Author 1)
Medical Student
Academic Level (Author 2)
Faculty
Discipline/Specialty (Author 2)
Neuroscience
Presentation Type
Poster
Discipline Track
Biomedical ENGR/Technology/Computation
Abstract Type
Research/Clinical
Abstract
Background: Marker-less motion capture systems have improved the assessment of human movement by allowing for a non-invasive, affordable, quantitative data collection useful in clinical settings. However, their use in real-world community care settings is largely underexplored, presenting a unique opportunity to bridge the gap between controlled laboratory environments and the complexity of providing care in diverse, everyday contexts. Here, we sought to review the current state of marker-less motion capture systems applied in community care, specifically for the monitoring and management of neurodegenerative diseases.
Methods: A systematic review was conducted using databases including PubMed, Web of Science, MEDLINE (NIH) and IEEE for studies published since 2010. Search terms utilized included "marker-less motion capture," "community," "ambulatory care," "long-term care," "public setting," "neurodegeneration," and related phrases. The papers were analyzed to identify key themes using Rayyan.ai, for themes such as tools and technologies employed, target populations, indoor versus outdoor applications, and the feasibility and advantage of real-world implementation.
Results: Overall, published work suggests that marker-less motion capture systems are increasingly recognized for their accuracy and scalability, however, transition to community care settings has been limited. Few studies have examined their use in public or residential environments, where real-world factors such as variable lighting, diverse patient populations, and dynamic movement patterns present unique challenges. Nonetheless, emerging evidence highlights their potential for enhancing care delivery, particularly in monitoring neurodegenerative conditions, tracking rehabilitation progress, and improving accessibility to high-quality movement analysis in underserved communities.
Conclusion: This review establishes a novel research trajectory by focusing on the feasibility, scalability, and transformative potential of marker-less motion capture systems in community care settings. Addressing this gap is essential to unlock their full potential, enabling patient-centered, equitable, and data-driven advancements in health care delivery. Future research should focus on overcoming technical challenges by integrating AI and wearable technologies for continuous monitoring. Additionally, dedicated efforts are important to create culturally relevant solutions aimed at reducing health disparities and ensuring improved care for marginalized communities.
Recommended Citation
Ganesh, Anjana and Baker, Kelsey, "Use of Marker-less Motion Capture Systems in Community Care Settings" (2025). Research Symposium. 19.
https://scholarworks.utrgv.edu/somrs/2025/posters/19
Included in
Use of Marker-less Motion Capture Systems in Community Care Settings
Background: Marker-less motion capture systems have improved the assessment of human movement by allowing for a non-invasive, affordable, quantitative data collection useful in clinical settings. However, their use in real-world community care settings is largely underexplored, presenting a unique opportunity to bridge the gap between controlled laboratory environments and the complexity of providing care in diverse, everyday contexts. Here, we sought to review the current state of marker-less motion capture systems applied in community care, specifically for the monitoring and management of neurodegenerative diseases.
Methods: A systematic review was conducted using databases including PubMed, Web of Science, MEDLINE (NIH) and IEEE for studies published since 2010. Search terms utilized included "marker-less motion capture," "community," "ambulatory care," "long-term care," "public setting," "neurodegeneration," and related phrases. The papers were analyzed to identify key themes using Rayyan.ai, for themes such as tools and technologies employed, target populations, indoor versus outdoor applications, and the feasibility and advantage of real-world implementation.
Results: Overall, published work suggests that marker-less motion capture systems are increasingly recognized for their accuracy and scalability, however, transition to community care settings has been limited. Few studies have examined their use in public or residential environments, where real-world factors such as variable lighting, diverse patient populations, and dynamic movement patterns present unique challenges. Nonetheless, emerging evidence highlights their potential for enhancing care delivery, particularly in monitoring neurodegenerative conditions, tracking rehabilitation progress, and improving accessibility to high-quality movement analysis in underserved communities.
Conclusion: This review establishes a novel research trajectory by focusing on the feasibility, scalability, and transformative potential of marker-less motion capture systems in community care settings. Addressing this gap is essential to unlock their full potential, enabling patient-centered, equitable, and data-driven advancements in health care delivery. Future research should focus on overcoming technical challenges by integrating AI and wearable technologies for continuous monitoring. Additionally, dedicated efforts are important to create culturally relevant solutions aimed at reducing health disparities and ensuring improved care for marginalized communities.
