Development of a Distributed Deep Learning Architecture to Train a Model for Classifying Human Activities Using Tensorflow Lite Running Locally on One or More bangle.JS Smartwatches

Introduction

Bangle.js is a JavaScript-based, individually programmable smartwatch. Furthermore, the device is able to run the Deep Learning framework TensorFlow Lite. Based on IMU-data collected by the smartwatch we want to train a local Deep Learning model which is able to classify human activities performed by the wearer. Models locally trained on the smartwatch(es), shall then interact with a globally trained model using ensemble or Federated Learning practices.

We expect this master thesis to be highly challenging, yet also bearing a high scientific potential. We therefore expect to follow-up the thesis with a scientific publication.

Tasks

• Development and implementation of JavaScript-based network architecture using the Bangle.js
• Locally train a Human Activity Recognition DL model on one or more Bangle.js
• (Optional) Align locally trained model with global meta-model (federated/ ensemble learning)
• Evaluation using state-of-the-art metrics (accuracy, precision, recall, F1-score, LOO cross-validation)
• Evaluation of reliability and energy efficiency
• Preparation of a written report in German or English
• Motivated and independent working

 Contact

Alexander Hölzemann ( alexander.hoelzemann@uni-sie.. )

Marius Bock ( marius.bock@uni-sie.. )