Deep Learning for Intake Gesture Detection From Wrist-Worn Inertial Sensors: The Effects of Data Preprocessing, Sensor Modalities, and Sensor Positions

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...

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Bibliographic Details
Main Authors: Hamid Heydarian, Philipp V. Rouast, Marc T. P. Adam, Tracy Burrows, Clare E. Collins, Megan E. Rollo
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Accelerometer
deep learning
intake gesture detection
gyroscope
wrist-worn
Online Access:https://ieeexplore.ieee.org/document/9187203/

Internet

https://ieeexplore.ieee.org/document/9187203/

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