Seminar: Resource-Aware Context Recognition using Deep Learning on Mobile Systems

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The aim of the seminar is to design knowledge extraction algorithms based on deep learning techniques for activity recognition in free-living using wearable sensing systems. Challenges will be related to imbalance of target classes, scarcity of computational resources and design of parallel computing coding structure. Evaluation will be performed on free-living dataset and benchmarked in terms of energy/memory savings, time constraints and quality of information retrieved. Prototyping will be done in Python using Keras and PyTorch libraries. 

Learning Objectives

  • Gain overview on the state-of-the-art of wearable technology for activity recognition.
  • Gain overview on the state-of-the-art of software-based power management for wearable devices.
  • Learn how to exploit deep learning  for signal processing and data abstraction.
  • Learn concepts of sparse sampling.

Course data

ECTS 2.5, 5, 7.5, default: 5
Project type Seminar, Extension to BSc.MSc.-Thesis can be discussed
Language English
Presence time 4 SWS
Work distribution 25% Theory, 75% Programming
Useful knowledge Python programming, machine learning basics
Period Summer semester 2019
First meeting Seminar introduction/Vorbesprechung on
24. Apr 2019, 17:00-18:30 at Henkestr. 91, Haus 7, 1. OG, R 373
Med. Eng. Seminar Title Advanced Context Recognition (ACR)


Giovanni Schiboni

Friedrich-Alexander-Universität Erlangen-Nürnberg