Deep Learning for Intake Gesture Detection From Wrist-Worn Inertial Sensors: The Effects of Data Preprocessing, Sensor Modalities, and Sensor Positions
Wrist-worn inertial measurement units have emerged as a promising technology to passively capture dietary intake data. State-of-the-art approaches use deep neural networks to process the collected inertial data and detect characteristic hand movements associated with intake gestures. In order to cla...
Main Authors: | Hamid Heydarian, Philipp V. Rouast, Marc T. P. Adam, Tracy Burrows, Clare E. Collins, Megan E. Rollo |
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Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9187203/ |
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