WASENN: Wavelet Assisted Stochastic Enabled Neural Network for Human Activity Recognition

Human activity recognition (HAR) is a challenging area of research with widespread applications in human-computer interaction. Recent advances in neural networks (NNs) have greatly improved the methods of HAR feature extraction from wearable sensor data and increased the interest in their classification using NNs. While most prior work has relied on software implementations of NN-based HAR, we investigate for the first time hardware implementations for use in resource-constrained edge devices. Emerging edge and near-sensor systems must avoid costly communication with the cloud and perform complex classification tasks locally. This points to using low-area hardware technology such as stochastic computing (SC) and enhanced feature extraction methods such as wavelet transform (WT). We explore the wavelet-assisted stochastic-enabled neural network (WASENN) design for HAR. The NN types we consider are convolutional neural networks and long short-term memory networks. We study both partial and full versions of WASENN and evaluate their performance and resource utilization on the UCI HAR and WISDM datasets. Our hardware synthesis results show the superiority of the wavelet transform in accuracy and size. They also show that SC reduces area and power by 32% and 74% respectively with little impact on classification accuracy.