Computational Architecture Proposal for Digital Optical Signal Processing in Human Gait Analysis

Markerless optical motion capture enables unobtrusive gait assessment, yet kinematic estimates remain sensitive to acquisition variability and to heterogeneous processing workflows, limiting cross-study comparability and reproducibility. This paper presents a modular computational architecture for processing markerless optical gait data, aimed at standardizing key steps from raw recordings to analysis-ready kinematic time series. Based on a structured comparison of commonly used pipelines and reported failure modes, the architecture specifies four sequential stages data acquisition, signal/pose preprocessing, gait-cycle segmentation, and representation/structuring and defines interfaces and quality-control checkpoints between modules. The pipeline integrates noise attenuation and normalization with a hybrid strategy: deterministic heuristics support rule-based quality screening and parameter initialization, while learning-based components target error-prone operations such as robust gait-cycle delineation under occlusions and variable viewing conditions. By explicitly separating concerns (capture, cleaning, segmentation, and representation) and by formalizing intermediate outputs and metadata, the proposed architecture provides an auditable foundation for implementation and subsequent experimental validation. The framework is intended to improve consistency of kinematic outputs and facilitate reproducible biomechanical analyses across laboratory and in-the-wild settings. Keywords: markerless motion capture; gait analysis; kinematic time series; signal preprocessing; gait-cycle segmentation; reproducible pipelines.