Improving the activity recognition using GMAF and transfer learning in post-stroke rehabilitation assessment

Abstract

An important part of developing a performant assessment algorithm for post-stroke rehabilitation is to achieve a high-precision activity recognition. Convolutional Neural Networks (CNN) are known to give very accurate results, however they require the data to be of a specific structure that differs from the sequential time-series format typically collected from wearable sensors. In this paper, we describe models to improve the activity recognition using the CNN classifier. At first by modifying the Gramian angular field algorithm by encoding all the sensors' channels from a single time window into a single 2D image allows to map the maximum activity characteristics. Feeding the resulting images to a simple 1D CNN classifier improves the accuracy of the test data from 94% for the traditional segmentation approach to 97.06%. Subsequently, we convert the 2D images into the RGB format and use a 2D CNN classifier. This results in increasing the test data accuracy to 97.52%. Finally, we employ transfer learning with the popular VGG_16 model to the RGB images, which yields to improving the accuracy further more to reach 98.53%.