2018 |
Papadopoulos, Alexandros; Kyritsis, Konstantinos; Sarafis, Ioannis; Delopoulos, Anastasios Personalised meal eating behaviour analysis via semi-supervised learning Inproceedings 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. |
Research
2018 |
Personalised meal eating behaviour analysis via semi-supervised learning Inproceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4768–4771, IEEE 2018. |