Embedded Neural Networks for Continuous Patient Posture Classification

Master thesis (2018)

Authors

V.R. Koeten Electrical Engineering, Mathematics and Computer Science

Contributors

Przemysław Pawełczak (supervisor 1)
F.A. Kuipers (supervisor 2)
Joana Gonçalves (supervisor 2)
Menno Gravemaker (supervisor 1)
Ide Swager (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Vincent Koeten
Neural Networks Embedded Systems Pressure ulcers Momo Medical Posture Classification
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Published Date

27-11-2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.