Maintaining mobility is vital for older adults. However, standardized functional tests often overlook crucial qualitative aspects, and expert assessments (EA) are costly and lack standardization. This project aims to develop an AI-based movement screening tool (SAT-Movement Analysis) utilizing the low-cost Skeleton Avatar Technique (SAT) and standardized Observational Movement Analysis (OMA) to detect deviations in daily movement. The initial phase automated expert assessments to establish a reliable foundation for machine learning. Five participants (ages 35–57) performed Sit-To-Stand, Stand-To-Sit, and One-Leg Stance, assessed by three physiotherapists using a modified IRAF protocol. Results demonstrated correspondence between automatically aggregated expert scores and consensus scores across all aggregation levels (Pearson’s r = 0.90–0.97, ICC = 0.91–0.98, = 0.78–1.00). These findings motivate continued development of an AI-trained screening tool providing accurate movement quality feedback based on 2D smartphone video, supporting early detection and personalized intervention.