Using mmWave Radar and Deep Learning to Classify Caregiver Activities for Infection Prevention

Koorosh Roohi, Atena Roshan Fekr

Abstract: Healthcare-associated infections (HAIs) pose significant risks in clinical environments, and the type of caregivers’ activities in patient room play a crucial role in infection transmission. To address this, we present an activity recognition system that uses 3D point cloud data from mmWave radar to classify caregivers’ actions and assess their associated infection risk levels while preserving their privacy. Our dataset, collected in a simulated hospital environment, includes 30 distinct caregiver activities categorized into four risk levels based on the type of contact: walking, environmental contact, low-risk patient contact, and high-risk patient contact. We evaluated three deep learning models—PointNet++, Pointformer, and DGCNN—using 5-fold cross-validation to determine the most effective approach for real-world deployment. PointNet++ achieved the best overall performance, with a classification accuracy of 75.16% ± 3.51% and an F1-score of 67.08% ± 2.15% when distinguishing all 30 activities. After grouping activities based on risk levels, performance improved significantly, with 90.84% ± 3.49% accuracy and 88.68% ± 1.85% F1-score. Our results demonstrate the feasibility of non-intrusive, privacy-preserving activity recognition using mmWave radar, enabling automated monitoring systems to enhance infection prevention strategies in clinical settings. Future work will focus on experiments with different validation methods and improving the deep learning models’ architectures.Clinical Relevance— This research helps the healthcare facilities such as Infection Prevention and Control (IPAC) experts to understand the events inside the patient rooms while not violating the privacy of patients and staff and predict and prevent possible infection transmission cases.