2018
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch Inproceedings
In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5511–5514, IEEE 2018.
Abstract | Links | BibTeX | Tags: Accelerometers, Gyroscopes, Hidden Markov models, Mouth, Sensors, Support vector machines, Training
@inproceedings{kyritsis2018end,
title = {End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/EMBC.2018.8513627},
year = {2018},
date = {2018-01-01},
booktitle = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {5511--5514},
organization = {IEEE},
abstract = {In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.},
keywords = {Accelerometers, Gyroscopes, Hidden Markov models, Mouth, Sensors, Support vector machines, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.
Papadopoulos, Alexandros; Kyritsis, Konstantinos; Sarafis, Ioannis; Delopoulos, Anastasios
Personalised meal eating behaviour analysis via semi-supervised learning Inproceedings
In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4768–4771, IEEE 2018.
Abstract | Links | BibTeX | Tags: Adaptation models, Entropy, Feature extraction, Hidden Markov models, Mouth, Support vector machines, Training
@inproceedings{papadopoulos2018personalised,
title = {Personalised meal eating behaviour analysis via semi-supervised learning},
author = {Alexandros Papadopoulos and Konstantinos Kyritsis and Ioannis Sarafis and Anastasios Delopoulos},
doi = {10.1109/EMBC.2018.8513174},
year = {2018},
date = {2018-01-01},
booktitle = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {4768--4771},
organization = {IEEE},
abstract = {Automated monitoring and analysis of eating behaviour patterns, i.e., “how one eats”, has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method.},
keywords = {Adaptation models, Entropy, Feature extraction, Hidden Markov models, Mouth, Support vector machines, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
Automated monitoring and analysis of eating behaviour patterns, i.e., “how one eats”, has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method.
2017
Kyritsis, Konstantinos; Tatli, Christina Lefkothea; Diou, Christos; Delopoulos, Anastasios
Automated analysis of in meal eating behavior using a commercial wristband IMU sensor Inproceedings
In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843–2846, IEEE 2017.
Abstract | Links | BibTeX | Tags: Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines
@inproceedings{kyritsis2017automated,
title = {Automated analysis of in meal eating behavior using a commercial wristband IMU sensor},
author = {Konstantinos Kyritsis and Christina Lefkothea Tatli and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/EMBC.2017.8037449},
year = {2017},
date = {2017-01-01},
booktitle = {2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {2843--2846},
organization = {IEEE},
abstract = {Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.},
keywords = {Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.