Lidar-Based Human Activity Recognition Through Laplacian Spectral Analysis

Human Activity Recognition supports applications in healthcare, manufacturing, and human–machine interaction. LiDAR point clouds offer a privacy-preserving alternative to cameras and are robust to illumination. We propose a HAR method based on graph spectral analysis. Each LiDAR frame is mapped to a proximity graph (ε-graph) and the Laplacian spectrum is computed. Eigenvalues and statistics of eigenvectors form pose descriptors, and temporal statistics over sliding windows yield fixed vectors for classification with Support Vector Machines and Random Forests. In the MM-Fi dataset with 40 subjects and 27 activities, under a strict subject-independent protocol, the method reaches 94.4% accuracy on a 13-class rehabilitation set and 90.3% on all 27 activities. It also surpasses the skeleton-based baselines reported for MM-Fi. A radius ablation shows that performance peaks at r=0.20 and decreases for smaller radii due to graph fragmentation and for larger radii due to over-dense connectivity. The contribution is a compact and interpretable feature set derived directly from point-cloud geometry that provides an accurate and efficient alternative to end-to-end deep learning and is applicable to any point cloud source.