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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Automatic Labelling for Chewing Timeseries- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Rui Zhang, M. Sc.
- Angaben
- Seminar
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch, Examination: Final presentation and final report
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, 1st. Floor, R 373
Vorbesprechung: 24.4.2019, 17:00 - 18:30 Uhr
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python, timeseries processing
- Contents
- Seminar description:
Chewing is an essential component of eating. Chewing cycles can be recorded using various sensors and detected applying appropriate algorithms. However, evaluating a chewing detection algorithm is usually difficult due to the lack of accurate ground truth. It is even more difficult to label free-living data of a tremendous number of chewing cycle instances. Based on our investigation, Temporalis electromyography (EMG) can serve as a reliable source of ground truth among a few other choices, e.g. videos and dietary diaries. The proposed student project aims at providing an accurate automatic chewing cycle labelling tool for EMG signals. The onsets and offsets of chewing cycles shall be detected and labelled. The labelling tool will be implemented in Python. The automatic labelling performance will be also evaluated compared with manual labels.
Learning objectives:
Python and graphical programming
Timeseries processing and chewing detection
- 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-automatic-labelling-for-chewing-timeseries/
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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