Sparse Feature Learning for Human Activity Recognition

초록

In this paper, rye propose an end-to-end deep learning model for human activity recognition. Our model is equipped with sparse learning, which absorbs a greater number of classes without making a significant change in the size of the model while sustaining the accuracy of existing classes. In addition, our model is lightweight than state-of-the-art models as we have utilized FCN-LSTM (Fully convolution network Long Short-term Memory). Our model predicts human activities such as walking, walking-upstairs, walking-downstairs, sitting, standing, and laying (total 6 classes). For validation of our deep learning model, we have utilized a well-known opensource dataset such as the UCI-HAR- dataset, which contains collections of smart-phones data of 30-subjects performing different activities with a smartphone. We evaluated the model using sparse learning and have shown that our model outperforms in learning features with few epochs with high accuracy and compact size, and efficient inference time correspondingly.

키워드