Real-Time Wristband Monitoring of Hand Hygiene Technique Using Motion Sensors and Machine Learning

Healthcare personnel (HCP) frequently do not perform recommended hand hygiene (HH) techniques, undermining infection prevention. The proposed wristband device is intended to dispense antiseptics at the point of care while providing real-time feedback on HH technique performance. Real-time feedback will be generated by an algorithm that classifies step-level hand movements from wrist inertial sensors. This algorithm is currently undergoing validation through assessment of concurrent validity against a gold standard, a trained observer who assesses the HH technique. The present study focuses on the development and validation of the acquisition and annotation protocol required for algorithm training and concurrent validity assessment prior to real-time clinical deployment. The protocol describes synchronized data collection using a temporal-marker IMU to delimit step boundaries, pairing segmented wrist-sensor recordings with observer-assigned correctness labels for each movement. Model development exploits sequence‑model architectures capable of dense time‑series labeling, and performance will be quantified using movement-level classification metrics (e.g., F1 score) and segment-overlap criteria (e.g., intersection‑over‑union) to reflect both correctness decisions and temporal segmentation quality. Secondary outcomes include antiseptic delivery performance, timeliness and usability of feedback, and user acceptability in clinical workflows. Data collected from a diverse sample of healthcare workers, comprising multiple supervised executions per participant, will support concurrent-validation analyses to determine the device's accuracy and potential to augment existing HH programs and reduce healthcare-associated infections.