Indoor Motion State Recognition using Denoising Autoencoders on Smartphone
Huan Dai, Chongben Tao, You Lu, Hong Lu · 2018 International Computers, Signals and Systems Conference (ICOMSSC) · 2018
The interest of indoor movement state recognition has been recently extensively evaluated by several research teams. This paper fully explores the signals from all the sensors in a smartphone and develops a novel deep neural network-based model. In the off-line training phase, this paper firstly proposes a matrix to pre-processing the raw data from a smartphone to overcome its redundancy and complexity. In a second place, this paper managed to go through a stacked denoising autoencoder neural network, resulting in an output layer with seven different indoor movement states. The trained network is then implemented on a smartphone and evaluated with online tests. Extensive experimental results demonstrate that the proposed method achieves 20% higher identification accuracy than other existing methods with respect to various indoor motion status (e.g., walking up and down stairs, taking elevator up and down).