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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


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.


  • 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


  • JavaScript (good – very good)
  • Machine/ Deep Learning & TensorFlow (good – very good)
  • Signal processing & sensors (good)

Working Materials

  • Bangle.js website (https://banglejs.com) & TensorFlow website (https://www.tensorflow.org/lite)
  • Guest talk Gordon Williams on Bangle.js at University of Siegen (https://youtu.be/73aBUkLissM)
  • Energy-efficient Human Activity Recognition using Adaptive CNNs (https://arxiv.org/abs/2102.01875)


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

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