Real-Time Classification of Operational Human Movements using YOLOv8-Pose and Feed-Forward Neural Networks

Abstract

This paper presents the technical framework of an integrated video analysis platform designed for the automated, real-time detection and classification of human movements. The system leverages the YOLOv81-pose model for robust 2 D human pose estimation, extracting critical upper-body keypoints (shoulders, elbows, wrists) from video streams. Subsequently, a custom-designed feed-forward neural network (specifically, a Multi-Layer Perceptron - MLP) classifies motion patterns based on features engineered from these keypoints. The feature engineering process incorporates normalization relative to body landmarks (inter-shoulder distance and body center) and the computation of relative angles between limb segments, providing pose invariance and discriminative power. The platform employs a modular pipeline encompassing video preprocessing (frame sampling at a target FPS), keypoint extraction with confidence thresholding, feature engineering, and model inference. This structured approach enables the system to accurately classify distinct operational movements, such as 'picking/placing' and 'planting' actions, as demonstrated on custom datasets. While the core components focus on single-stream processing and model training, the platform's modular design supports potential extensions towards distributed architectures for scalable processing of data from multiple camera sources. The developed system holds significant potential for applications in industrial automation, human-robot collaboration, ergonomic assessments, and workplace safety monitoring. © 2025 Elsevier B.V., All rights reserved.