Deep learning framework for human activity recognition based on sensor fusion in smartphones - San Buenaventura, Charlene V.
Smartphones are ubiquitous devices that offer endless possibilities for health-related applications such as Ambient Assisted Living (AAL). They are rich in sensors that can be used for human activity recognition and monitoring. The emerging problem now is the selection of proper methods to accurately and efficiently perform human activity recognition (HAR) in a resource and computationally constrained environment. Although machine learning-based HAR studies have been successful in the past, several design struggles can be easily resolved with deep learning. In this study, we examine the use of both convolutional and recurrent neural networks in dealing with common challenges in smartphone-based HAR such as device location and subject dependency, as well as feature extraction. It was found that CNN is capable of providing both location- and subject-independent recognition with classification rates of 98.31% and 90.61%, respectively, while RNN was only able to achieve location-independent recognition of 97.17%. Furthermore, the recognition performance of accelerometer, gyroscope and magnetometer when used separately and in fusion is examined to gain valuable knowledge that can be used in dynamic sensing and data collection. Finally, hyper-parameter optimization using Bayesian Optimization was performed to find the optional hyper-parameter settings of the network.