https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 OREBA: a dataset for objectively recognizing eating behavior and associated intake https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38697 Thu 13 Jan 2022 14:41:12 AEDT ]]> Using deep learning to assess eating behaviours with wrist-worn inertial sensors https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40792 Mon 25 Jul 2022 16:24:58 AEST ]]> Assessing eating behaviour using upper limb mounted motion sensors: a systematic review https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35694 Fri 25 Oct 2019 12:31:21 AEDT ]]> Deep learning for intake gesture detection from wrist-worn inertial sensors: the effects of data preprocessing, sensor modalities, and sensor positions https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39083 1=.778 ). As for data preprocessing, results show that applying a consecutive combination of mirroring, removing gravity effect, and standardization was beneficial for model performance, while smoothing had adverse effects. We further investigate the effectiveness of using different combinations of sensor modalities (i.e., accelerometer and/or gyroscope) and sensor positions (i.e., dominant intake hand and/or non-dominant intake hand).]]> Fri 06 May 2022 15:21:38 AEST ]]>