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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Resource-Aware Context Recognition using Deep Learning on Mobile Systems- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Giovanni Schiboni, M. Sc.
- Angaben
- Seminar
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch
Zeit und Ort: n.V.; Bemerkung zu Zeit und Ort: First meeting (24.04.2019, 17:00 - 18:30) and seminar held at MVC, Henkestr. 91, House 7, 1st. Floor, R 373
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python programming, machine learning basics
- Contents
- Seminar Description:
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.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2019/03/05/seminar-sensor-data-compression-and-power-management-for-deep-learning-based-activity-recognition/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Dienstag, 2.4.2019 bis Donnerstag, 18.4.2019 über: StudOn.
- Institution: Lehrstuhl für Digital Health
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