Title
Embedded Neural Networks for Continuous Patient Posture Classification
Author
Koeten, Vincent (TU Delft Electrical Engineering, Mathematics and Computer Science)
Contributor
Pawelczak, Przemek (mentor)
Kuipers, Fernando (graduation committee)
de Pinho Goncalves, Joana (graduation committee)
Gravemaker, Menno (mentor)
Swager, Ide (mentor)
Degree granting institution
Delft University of Technology
Date
2018-11-27
Abstract
Current hospital protocols dictate patients be turned at least every three hours in the effort of preventing pressure ulcers. To reduce the workload of nurses, Momo Medical has created an embedded sensing device to track the patient's posture and notify nurses when it is time to turn them. The challenge presented and the focus of this thesis is classifying the posture of the patient based on the sensor data sampled, specifically, utilizing neural networks on an embedded platform.Furthermore an optimization inspired by recurrent neural networks and ensemble neural networks is proposed, implemented, and compared against vanilla neural networks and pruned variants.
Subject
Neural Networks
Embedded Systems
Posture Classification
Momo Medical
pressure ulcers
To reference this document use:
http://resolver.tudelft.nl/uuid:c8852206-ea81-4e59-bdc4-aa7d673f1f69
Embargo date
2021-11-30
Part of collection
Student theses
Document type
master thesis
Rights
© 2018 Vincent Koeten