  The application of deep learning to nursing procedure activity understanding
has the potential to greatly enhance the quality and safety of nurse-patient
interactions. By utilizing the technique, we can facilitate training and
education, improve quality control, and enable operational compliance
monitoring. However, the development of automatic recognition systems in this
field is currently hindered by the scarcity of appropriately labeled datasets.
The existing video datasets pose several limitations: 1) these datasets are
small-scale in size to support comprehensive investigations of nursing
activity; 2) they primarily focus on single procedures, lacking expert-level
annotations for various nursing procedures and action steps; and 3) they lack
temporally localized annotations, which prevents the effective localization of
targeted actions within longer video sequences. To mitigate these limitations,
we propose NurViD, a large video dataset with expert-level annotation for
nursing procedure activity understanding. NurViD consists of over 1.5k videos
totaling 144 hours, making it approximately four times longer than the existing
largest nursing activity datasets. Notably, it encompasses 51 distinct nursing
procedures and 177 action steps, providing a much more comprehensive coverage
compared to existing datasets that primarily focus on limited procedures. To
evaluate the efficacy of current deep learning methods on nursing activity
understanding, we establish three benchmarks on NurViD: procedure recognition
on untrimmed videos, procedure and action recognition on trimmed videos, and
action detection. Our benchmark and code will be available at
\url{https://github.com/minghu0830/NurViD-benchmark}.
