Human Activities Recognition with a Single Writs IMU via a Variational Autoencoder and Android Deep Recurrent Neural Nets

Edwin Valarezo Añazco, Patricio Rivera Lopez, Hyemin Park, Nahyeon Park, Tae-Seong Kim

Human Activity Recognition (HAR) is an active research field because of its versatility towards various application areas such as healthcare and lifecare. In this study, a novel HAR system is proposed based on an autoencoder for denoising and Recurrent Neural Network (RNN) for classification with a single Inertial Measurement Unit (IMU) located on a dominant wrist. A Variational Autoencoder (VAE) is built to denoise IMU signals which improves HAR by Android Deep RNN. Evaluating our VAE and Android Deep RNN HAR system is done in two ways. First, the system is tested on a PC using discrete epochs of activities of daily living. Our results show that VAE improves Signal-to-Noise Ratio (SNR) of the IMU signal from 8.78 to 17.26 dB. In turn, HAR improves from 89.29% to 95.11% in F1-score and from 90.38% to 95.47% in accuracy. Secondly, the system is tested on an Android device (i.e., smartphone) using continuous activity signals. This is done by transferring the PC HAR system to an Android HAR App (i.e., Android Deep RNN). We have achieved 86.13% and 95.09% in accuracy without and with VAE respectively. Our results demonstrate that HAR can be achieved in real-time on a standalone smart device with a single IMU for lifelogging services.