Machine Learning-Based Human Action Estimation for Interactive Spatial Lighting Control

This study investigates the integration of machine learning and architectural lighting design by proposing a proof-of-concept adaptive lighting system driven by human actions and spatial position. A custom video dataset was created based on five actions—standing, sitting, walking, running, and dancing—and three positional categories within a defined space. Two different machine learning approaches were evaluated for human action recognition: a skeleton-based model using MediaPipe pose extraction with an LSTM architecture, and a pixel-based approach combining feature extraction from raw video frames with an MLP classifier. The classified action and position data were mapped to pre-defined lighting schemes generated parametrically using Grasshopper, enabling context-aware lighting recommendations. The results show that while action classification accuracy is limited due to dataset size, position recognition achieves high reliability. The study highlights the potential of action-oriented, human-centered lighting systems and outlines directions for future research involving larger datasets and user-centered evaluations.