  In an aging population, elderly patient safety is a primary concern at
hospitals and nursing homes, which demands for increased nurse care. By
performing nurse activity recognition, we can not only make sure that all
patients get an equal desired care, but it can also free nurses from manual
documentation of activities they perform, leading to a fair and safe place of
care for the elderly. In this work, we present a multimodal transformer-based
network, which extracts features from skeletal joints and acceleration data,
and fuses them to perform nurse activity recognition. Our method achieves
state-of-the-art performance of 81.8% accuracy on the benchmark dataset
available for nurse activity recognition from the Nurse Care Activity
Recognition Challenge. We perform ablation studies to show that our fusion
model is better than single modality transformer variants (using only
acceleration or skeleton joints data). Our solution also outperforms
state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based
classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is
available at \url{https://github.com/Momilijaz96/MMT_for_NCRC}.
