Multi-attention deep recurrent neural network for nursing action evaluation using wearable sensor

Abstract

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.