Nurse care activity recognition based on convolution neural network for accelerometer data

Human activity recognition on sensor data plays a vital role in health monitoring and elderly care service monitoring. Although tremendous progress has been noticed to the use of sensor technology to collect activity recognition data, recognition still remains challenging due to the pervasive nature of the activities. In this paper, we present a Convolution Neural Network (CNN) model by our team DataDrivers_BD in "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" which is quite challenging because of the similarity among the tasks. On the other hand, the dissimilarity among the users patterns of working for a particular task. Since CNN can retrieve informative features automatically, it has become one of the most prominent methods in activity recognition. Our extensive experiment on nurse care activity recognition challenge dataset also achieved significant accuracy of 91.59% outperforming the existing state of the art algorithms.