GhostHandEgoNet: Lightweight Egocentric Hand Pose Estimation for Embedded Hand Hygiene Monitoring

Proper hand hygiene is critical for infection prevention, yet real-world compliance and quality assessment remain challenging in busy clinical environments. This paper presents a lightweight end-to-end hand hygiene monitoring system that integrates egocentric hand pose estimation, temporal step recognition, and soap-foam verification for real-time embedded deployment. Hand keypoints are obtained using either MediaPipe or our custom GhostHandEgoNet, which leverages Ghost convolutions with attention to improve robustness under occlusion while maintaining a compact footprint; it achieves an end-point error below 14 pixels with only 2.56M parameters and improves keypoint accuracy by over 90% compared with MediaPipe in occluded scenes. A three-layer Long Short-Term Memory (LSTM) model continuously skeletonizes sequences to classify the standardized seven-step handwashing protocol, achieving accuracies of 93.5%, 97.1%, and 94.3% on the Kaggle, METC, and in-house hospital datasets, respectively. To assess washing quality beyond motion analysis, a modified Fast-SCNN module performs foam segmentation and sufficiency classification in a single forward pass, reaching a foam mIoU above 85%. Compared with the Transformer and ST-GCN based backbones, our method achieves the lowest average latency of 87.66 ms/frame and the highest throughput of 11.41 FPS on the NVIDIA Jetson AGX Xavier platform. These results demonstrate that our approach not only delivers competitive classification accuracy but also satisfies the stringent real-time and memory constraints required for practical embedded deployment, making it particularly suitable for on-device hand hygiene monitoring in clinical and public health settings.