Paper seminar by Odongo Steven Eyobu. Presented on: 2017-11-16
Title: Human activity recognition with smartphone sensors using deep learning neural networks
Authors: Charissa Ann Ronao, Sung-Bae Cho.
Journal publisher: Expert Systems with Applications
The article proposes:
The use of deep convolutional networks (convnets) for efficient and effective human activity recognition using smartphone inertial measurement unit (IMU) sensor data.
a. They claim that convnets provide an efficient way to automatically and adaptively extract robust features from raw sensor data.
b. By varying different temporal correlation sizes and pooling sizes, optimal dimensions of both parameters can be found through a greedy-wise search manner.
c. The final results in the article shows that the convent can achieve upto 94.79 accuracy on raw sensor test data and 95.75% with additional information of temporal fast fourier transform of the HAR datasert.