A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms

Abstract Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems

Achieving adherence in home-based rehabilitation with novel human machine interactions that stimulate community-dwelling older adults

Balance disorders are expressed with main symptoms of vertigo, dizziness instability and disorientation. Most of them are caused by inner ear pathologies, but neurological, medical and psychological factors are also responsible. Balance disorders overwhelmingly affect daily activities and cause psychological and emotional hardship. They are also the main cause of falls which are a global epidemic. Home based balance rehabilitation is an effective approach for alleviating symptoms and for improving balance and self-confidence. However, the adherence in such programs is usually low with lack of motivation and disease related issues being the most influential factors. Holobalance adopts the Capability, Opportunity and Motivation (COM) and Behaviour (B) model to identify the sources of the behaviour that should be targeted for intervention and proposes specific Information Technology components that provide the identified interventions to the users in order to achieve the target behavioural change, which in this case is adherence to home base rehabilitationPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Pre-final version. For the citation of this conference paper please use the DOI provided of the publisher (IEEE) for the final version which is the following one: 10.1109/RBME.2018.285505

Achieving Adherence in Home-Based Rehabilitation with Novel Human Machine Interactions that Stimulate Community-Dwelling Older Adults

Balance disorders are expressed with main symptoms of vertigo, dizziness instability and disorientation. Most of them are caused by inner ear pathologies, but neurological, medical and psychological factors are also responsible. Balance disorders overwhelmingly affect daily activities and cause psychological and emotional hardship. They are also the main cause of falls which are a global epidemic. Home based balance rehabilitation is an effective approach for alleviating symptoms and for improving balance and self-confidence. However, the adherence in such programs is usually low with lack of motivation and disease related issues being the most influential factors. Holobalance adopts the Capability, Opportunity and Motivation (COM) and Behaviour (B) model to identify the sources of the behaviour that should be targeted for intervention and proposes specific Information Technology components that provide the identified interventions to the users in order to achieve the target behavioural change, which in this case is adherence to home base rehabilitation