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Article

Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels

1
Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
2
Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
3
Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia
*
Author to whom correspondence should be addressed.
Academic Editor: Angelo Maria Sabatini
Sensors 2021, 21(5), 1902; https://doi.org/10.3390/s21051902
Received: 31 January 2021 / Revised: 2 March 2021 / Accepted: 4 March 2021 / Published: 9 March 2021
(This article belongs to the Special Issue New Frontiers in Sensor-Based Activity Recognition)
Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist. View Full-Text
Keywords: activity recognition; automated dietary assessment; smartwatch; inertial sensors; data selection; information fusion activity recognition; automated dietary assessment; smartwatch; inertial sensors; data selection; information fusion
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MDPI and ACS Style

Stankoski, S.; Jordan, M.; Gjoreski, H.; Luštrek, M. Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels. Sensors 2021, 21, 1902. https://doi.org/10.3390/s21051902

AMA Style

Stankoski S, Jordan M, Gjoreski H, Luštrek M. Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels. Sensors. 2021; 21(5):1902. https://doi.org/10.3390/s21051902

Chicago/Turabian Style

Stankoski, Simon, Marko Jordan, Hristijan Gjoreski, and Mitja Luštrek. 2021. "Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels" Sensors 21, no. 5: 1902. https://doi.org/10.3390/s21051902

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