Evolution of a Real-Time Markerless Kinematic Posture-Assessment System: From Multi-Participant Monitoring to Depth-Aware Angular Assessment

Markerless human-motion assessment has become increasingly relevant in domains such as psychomotor learning, rehabilitation, sports training, and collaborative educational environments. While recent pose-estimation frameworks provide reliable skeletal reconstruction, translating these data into interpretable posture assessment remains challenging, particularly under varying participant-to-sensor distances and in multi-participant settings. This paper presents the technological evolution from KUMITRON, a system originally developed for synchronized multi-participant monitoring and interaction analysis in psychomotor activities, to COLLVIT, a prototype implementing configurable depth-aware joint-angle posture assessment. This evolution encompasses RGB-D integration, configurable angular templates, depth-informed adaptive tolerance mechanisms, temporal stabilization strategies, interpretable posture assessment, and multi-participant processing capabilities. The paper distinguishes between the architectures defined at the patent level, their implementation through the KUMITRON and COLLVIT prototypes, and capabilities that remain targets for future implementation and validation. Rather than reporting a controlled experimental evaluation, the contribution of this work lies in documenting the technological evolution from KUMITRON to COLLVIT, the rationale behind the patent-based design decisions, the implementation of the current prototype, and directions for its future development and validation. The resulting framework provides a flexible foundation for future research on markerless kinematic posture assessment in educational, rehabilitation, and other psychomotor application domains.