T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis

• URL: http://arxiv.org/abs/2012.05456v1
• Date: Thu, 10 Dec 2020 05:07:28 GMT
• Title: T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis
• Authors: Minhao Liu, Ailing Zeng, Qiuxia Lai, Qiang Xu
• Abstract summary: We propose a novel tree-structured wavelet neural network for sensor data analysis, namely emphT-WaveNet. T-WaveNet provides more effective representation for sensor information than existing techniques, and it achieves state-of-the-art performance on various sensor datasets.
• Score: 9.449017120452675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural networks (DNNs) are shown to be more effective than conventional hand-crafted ones. However, most of these solutions rely solely on the network to extract application-specific information carried in the sensor data. Motivated by the fact that usually a small subset of the frequency components carries the primary information for sensor data, we propose a novel tree-structured wavelet neural network for sensor data analysis, namely \emph{T-WaveNet}. To be specific, with T-WaveNet, we first conduct a power spectrum analysis for the sensor data and decompose the input signal into various frequency subbands accordingly. Then, we construct a tree-structured network, and each node on the tree (corresponding to a frequency subband) is built with an invertible neural network (INN) based wavelet transform. By doing so, T-WaveNet provides more effective representation for sensor information than existing DNN-based techniques, and it achieves state-of-the-art performance on various sensor datasets, including UCI-HAR for activity recognition, OPPORTUNITY for gesture recognition, BCICIV2a for intention recognition, and NinaPro DB1 for muscular movement recognition.

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