XFMP: A Benchmark for Explainable Fine-grained Abnormal Behavior Recognition on Medical Personal Protective Equipment

The proper use of medical personal protective equipment (MPPE) is critical for frontline healthcare workers (HCWs) to handle highly contagious diseases. Due to the complexity of PPE donning and doffing protocols, public health organizations typically recommend having trained observers monitor the entire PPE donning and doffing process, preventing self-contamination and transmission. However, the high costs of manual monitoring impede the implementation of this practice, which makes AI-assisted PPE monitoring highly valuable. Some studies have applied computer vision techniques to PPE monitoring, but they have only focused on limited integrity checks at donning completion, which is unable to provide real-time warnings for abnormal actions during the doffing process. Furthermore, model practicality and user-friendliness are constrained by the lack of explainability. To address this, we propose an explainable and fine-grained dataset for MPPE doffing monitoring called the XFMP dataset. The dataset contains 3596 expert-annotated samples over three sub-tasks: doffing stage classification (DSC), abnormal action recognition (AAR), and critical region localization (CRL). Accordingly, we introduce multi-dimensional evaluation metrics for XFMP and a multitask human behavior semantic attention network (MHBSAN). Experiments demonstrate that MHBSAN outperforms alternative approaches, achieving 0.968/0.855 accuracy for stage/action classification and 0.791 Top-1 Loc@0.5 for CRL sub-task. Moreover, it demonstrates exceptional adaptability across different healthcare environments. Ablation studies and case analyses further validate the contributions and efficacy of the proposed model regarding classification and explainability.