Multi-attention deep recurrent neural network for nursing action evaluation using wearable sensor
Multi-attention deep recurrent neural network for nursing action evaluation using wearable sensor
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DOI：
10.1145/3377325.3377530
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Zhihang Zhong;Chingszu Lin;T. Ogata;J. Ota
中科院分区：
文献类型：
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作者：
Zhihang Zhong;Chingszu Lin;T. Ogata;J. Ota
一种能够评估学生实践患者处理相关护理技能表现的护理行动评估系统成为解决护理教育者短缺问题的迫切需要。这样的评估系统的设计应减少手工制作的程序，以实现其可扩展性。此外，实现高精度的护理动作识别，特别是细粒度的动作识别仍然是一个问题。这体现在学生进行护理动作时对正确和错误方法的认识，准确率低就会误导护生。我们提出了一种多注意深度循环神经网络（MA-DRNN）模型，通过直接处理来自可穿戴传感器的原始加速度和旋转速度信号来识别护理动作。收集称为患者转移的护理技能中的目标护理行动的数据样本，以训练和比较模型。实验结果表明，在时域和层域注意力机制的帮助下，该模型对四个目标细粒度护理动作类别的识别准确率达到约 96%，优于基于可穿戴传感器的 HAR 的最新模型。
A nursing action evaluation system that can assess the performance of students practicing patient handling related nursing skills becomes an urgent need for solving the nursing educator shortage problem. Such an evaluation system should be designed with less hand-crafted procedures for its scalability. Additionally, realizing high accuracy of nursing action recognition, especially fine-grained action recognition remains a problem. This reflects in the recognition of the correct and incorrect methods when students perform a nursing action, and low accuracy of that would mislead the nursing students. We propose a multi-attention deep recurrent neural network (MA-DRNN) model for nursing action recognition by directly processing the raw acceleration and rotational speed signals from wearable sensors. Data samples of target nursing actions in a nursing skill called patient transfer were collected to train and compare the models. The experiment results show that the proposed model can reach approximately 96% recognition accuracy for four target fine-grained nursing action classes helped by the attention mechanism on time and layer domains, which outperforms the state-of-the-art models of wearable sensor-based HAR.