Activity Recognition using Fully Convolutional Network from Smartphone Accelerometer
Mooseop Kim, Chi Yoon Jeong, Hyung Cheol Shin · 2018
This paper presents an activity recognition using smartphone built-in accelerometer. One of the most important issues in implementing activity recognition on embedded systems, including smartphones, is to achieve a high accuracy with a low computational cost and low memory usage. In this paper, we propose an activity recognition using the fully convolutional networks and introduce a new method to generate an input signal image using the combination of deep features and orientation-independent features. The experimental results show that the proposed method is able to achieve a high accuracy with a low memory usage.