TOPIC 425: POSTURE ANALYSIS THROUGH MACHINE LEARNING (PATHML) PHASE II

The availability of low-cost video has tremendous potential to provide new insight into how physical behavior is associated with health, define clinical trial outcomes, and assess physical function within the home, research and clinical environments. The COVID-19 pandemic required a rapid shift to incorporate video-based data collection and clinical visits, establishing the feasibility of using video for clinical outcome assessments (COA). However, there is a lack of software tools to automate and incorporate video into health-related applications. Our Phase I project resulted in a proof-of-concept PathML software prototype that included several sufficiently accurate machine learning models to annotate posture, physical activity intensity, and features of the environment. The Phase II project has two goals. The first goal is to enhance the functionality of the Phase I PathML prototype through innovative machine learning approaches to annotate relevant to physical behavior research taxonomies and UX enhancements that will result in a market-ready tool addressing the needs of physical behavior researchers. Additionally, this project seeks to extend the software to include automatic identification and scoring of clinically relevant COA’s (e.g., 30 second sit-stand test, two-minute walk test), partnering with clinical researchers with expertise in aging, movement disorders, amputees, and cancer rehabilitation.