论文标题
用于护理活动识别的多模式变压器
Multimodal Transformer for Nursing Activity Recognition
论文作者
论文摘要
在老龄化的人口中，老年患者安全是医院和疗养院的主要关注点，这需要增加护士护理。通过表现护士活动的认可，我们不仅可以确保所有患者都得到同等的期望护理，而且还可以使护士免于手动记录他们执行的活动，从而为老年人提供公平且安全的护理场所。在这项工作中，我们提出了一个基于多模式变压器的网络，该网络从骨骼关节和加速数据中提取特征，并融合他们执行护士活动识别。我们的方法在基准数据集上实现了81.8％精度的最新性能，可从护士护理活动识别挑战中提供护士活动识别。我们进行消融研究，以表明我们的融合模型比单一模态变压器变体（仅使用加速度或骨架关节数据）更好。在NCRC数据集上，我们的解决方案还优于最先进的ST-GCN，GRU和其他经典基于手工制作的基于基于功能的分类器解决方案。代码可在\ url {https://github.com/momilijaz96/mmt_for_ncrc}中获得。
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}.
