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The application of deep learning to nursing procedure activity understandinghas the potential to greatly enhance the quality and safety of nurse-patientinteractions. By utilizing the technique, we can facilitate training andeducation, improve quality control, and enable operational compliancemonitoring. However, the development of automatic recognition systems in thisfield is currently hindered by the scarcity of appropriately labeled datasets.The existing video datasets pose several limitations: 1) these datasets aresmall-scale in size to support comprehensive investigations of nursingactivity; 2) they primarily focus on single procedures, lacking expert-levelannotations for various nursing procedures and action steps; and 3) they lacktemporally localized annotations, which prevents the effective localization oftargeted actions within longer video sequences. To mitigate these limitations,we propose NurViD, a large video dataset with expert-level annotation fornursing procedure activity understanding. NurViD consists of over 1.5k videostotaling 144 hours, making it approximately four times longer than the existinglargest nursing activity datasets. Notably, it encompasses 51 distinct nursingprocedures and 177 action steps, providing a much more comprehensive coveragecompared to existing datasets that primarily focus on limited procedures. Toevaluate the efficacy of current deep learning methods on nursing activityunderstanding, we establish three benchmarks on NurViD: procedure recognitionon untrimmed videos, procedure and action recognition on trimmed videos, andaction detection. Our benchmark and code will be available at\url{https://github.com/minghu0830/NurViD-benchmark}.

NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding