Navigation CNN

Convolutional Algorithms for Real-Time Data Processing with Time Series on Smartphones for Navigation and Tracking

 Neural Network, Thought, Mind, Mental

Problem Statement: 

Most modern smartphones have various sensor types integrated and become a rich sensing platform for a wide range of applications including healthcare, sport, social media, environmental monitoring. In most of the applications, time-serial data such as the Inertial Measurement Unit (IMU) data is streamed continuously to the application and processed using a Convolutional Neural Network (CNN) model which typically uses the standard 2D convolution. However, there are many more convolution types (including depth-wise, dilated, transposed) that have not been exploited for real-time computation in an embedded device such as a smartphone.

 To this end, this project aims at exploiting the various kind of CNN convolutions for IMU-based tracking.


The project will be divided into 2 main tasks:

  1. Study IMU-based tracking using Convolutional Neural Networks (CNN) with various type of convolutions
  2. Implement the models on a smartphone for a tracking application.


30% Theory, 50% Implementation, 20%Writing


Le Viet Duc,, room ZI 5013