1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition

Abstract

Human activity recognition aims to determine the actions or behavior of a person based on the time series data. In recent year, more large human activity recognition datasets are available as it can be collected in easier and cheaper ways. In this work, a 1D Convolutional Neural Network with Long Short-Term Memory Network for human activity recognition is proposed. The 1D Convolutional Neural Network is employed to learn high-level representative features from the accelerometer and gyroscope signal data. The Long Short-Term Memory network is then used to encode the temporal dependencies of the features. The final classification is performed with a softmax classifier. The proposed 1D Convolutional Neural Network with Long Short-Term Memory Network is evaluated on MotionSense, UCI-HAR, and USC-HAD datasets. The class distributions of these datasets are imbalanced. In view of this, adjusted class weight is proposed to mitigate the imbalanced class issue. Furthermore, early stopping is utilized to reduce the overfitting in the training. The proposed method achieved promising performance on MotionSense, UCI-HAR, and USC-HAD datasets, with F1-score of 98.14%, 91.04%, and 76.42%, respectively.