2019
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
Detecting meals in the wild using the inertial data of a typical smartwatch Inproceedings
In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4229–4232, IEEE 2019.
Abstract | Links | BibTeX | Tags: Accelerometers, Artificial neural networks, Detection algorithms, Feature extraction, Gyroscopes, Lenses, Monitoring
@inproceedings{kyritsis2019detecting,
title = {Detecting meals in the wild using the inertial data of a typical smartwatch},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/EMBC.2019.8857275},
year = {2019},
date = {2019-01-01},
booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {4229--4232},
organization = {IEEE},
abstract = {Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to-End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804.},
keywords = {Accelerometers, Artificial neural networks, Detection algorithms, Feature extraction, Gyroscopes, Lenses, Monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}
Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to-End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804.