Est-ce que l'apprentissage automatique permet de prédire un comportement en nutrition?

L'apprentissage automatique (AA) a permis des progrès inégalés en nutrition, notamment dans les domaines de l'évaluation alimentaire, du traitement de données massives associées aux sciences « omiques », de l'analyse des médias sociaux et de la prédiction du risque de maladie. Toutefois, l'AA n'est pas encore exploité dans le domaine de la prédiction de comportements associés à la saine alimentation. Les interventions et politiques de santé publique en nutrition mises sur pied jusqu'à ce jour ne semblent pas porter fruit puisque les choix et comportements alimentaires au niveau populationnel restent sous-optimaux. Afin de contrer l'épidémie de maladies chroniques qui découle d'une alimentation sous-optimale au Québec, il est essentiel d'identifier les facteurs individuels, sociaux et environnementaux qui déterminent les choix alimentaires de la population. Plusieurs études soutiennent l'idée que les algorithmes d'AA ont une meilleure capacité de prédiction que des modèles statistiques traditionnels, et pourraient donc permettre de mieux documenter les facteurs qui influencent les choix alimentaires de la population. Cependant, d'autres études n'ont rapporté aucune valeur ajoutée de l'utilisation d'algorithmes d'AA pour la prédiction du risque de maladies par rapport à des approches prédictives plus traditionnelles. L'objectif de ce projet de maîtrise était donc de comparer la performance de neuf algorithmes d'AA à celle de deux modèles statistiques traditionnels pour prédire un comportement en nutrition, soit une consommation adéquate de légumes et fruits, à partir de 525 variables individuelles, sociales et environnementales reliées aux habitudes alimentaires. Les résultats de ce mémoire démontrent que les algorithmes d'AA ne prédisent pas mieux la consommation adéquate de légumes et fruits que les modèles statistiques traditionnels. Cependant, étant une des premières études à comparer les algorithmes d'AA à des modèles statistiques traditionnels pour prédire un comportement en nutrition, davantage d'études comparant les deux approches doivent être menées afin d'identifier celles qui nous permettront de mieux documenter les déterminants de la saine alimentation.Machine learning (ML) has offered unparalleled opportunities of progress in nutrition, including in the fields of dietary assessment, omics data analysis, social media data analysis and diet-related health outcome prediction. However, ML has not yet been explored for the prediction of dietary behaviours. Despite several public health interventions and policies in nutrition, adhering to heathy eating remains a challenge. In order to address the epidemic of chronic disease caused by unhealthy eating habits, it is necessary to better identify the individual, social and environmental determinants of healthy eating in the Quebec population. Many studies demonstrate that ML algorithms predict health outcomes with higher accuracy than traditional statistical models, and thus, could allow better identifying the factors that influence food choices in the Quebec population. However, other studies have reported no added value of using ML algorithms for disease risk prediction compared to traditional approaches. The aim of this master's project was to compare the accuracy of nine ML algorithms and two traditional statistical models to predict adequate vegetable and fruit consumption using a large array of individual, social and environmental variables. The results of this study demonstrate that ML algorithms do not predict adequate vegetable and fruit consumption with higher accuracy than traditional statistical models. However, being one of the first studies to compare ML algorithms and traditional statistical models to predict dietary behaviours, more studies comparing both approaches are needed to determine which models will allow better identifying the determinants of healthy eating

Detecting Periods of Eating in Everyday Life by Tracking Wrist Motion — What is a Meal?

Eating is one of the most basic activities observed in sentient animals, a behavior so natural that humans often eating without giving the activity a second thought. Unfortunately, this often leads to consuming more calories than expended, which can cause weight gain - a leading cause of diseases and death. This proposal describes research in methods to automatically detect periods of eating by tracking wrist motion so that calorie consumption can be tracked. We first briefly discuss how obesity is caused due to an imbalance in calorie intake and expenditure. Calorie consumption and expenditure can be tracked manually using tools like paper diaries, however it is well known that human bias can affect the accuracy of such tracking. Researchers in the upcoming field of automated dietary monitoring (ADM) are attempting to track diet using electronic methods in an effort to mitigate this bias. We attempt to replicate a previous algorithm that detects eating by tracking wrist motion electronically. The previous algorithm was evaluated on data collected from 43 subjects using an iPhone as the sensor. Periods of time are segmented first, and then classified using a naive Bayesian classifier. For replication, we describe the collection of the Clemson all-day data set (CAD), a free-living eating activity dataset containing 4,680 hours of wrist motion collected from 351 participants - the largest of its kind known to us. We learn that while different sensors are available to log wrist acceleration data, no unified convention exists, and this data must thus be transformed between conventions. We learn that the performance of the eating detection algorithm is affected due to changes in the sensors used to track wrist motion, increased variability in behavior due to a larger participant pool, and the ratio of eating to non-eating in the dataset. We learn that commercially available acceleration sensors contain noise in their reported readings which affects wrist tracking specifically due to the low magnitude of wrist acceleration. Commercial accelerometers can have noise up to 0.06g which is acceptable in applications like automobile crash testing or pedestrian indoor navigation, but not in ones using wrist motion. We quantify linear acceleration noise in our free-living dataset. We explain sources of noise, a method to mitigate it, and also evaluate the effect of this noise on the eating detection algorithm. By visualizing periods of eating in the collected dataset we learn that that people often conduct secondary activities while eating, such as walking, watching television, working, and doing household chores. These secondary activities cause wrist motions that obfuscate wrist motions associated with eating, which increases the difficulty of detecting periods of eating (meals). Subjects reported conducting secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals, compared to only 6.8% of the time in a cafeteria dataset. Walking motion was found during 5.5% of the time during meals in free-living, compared to 0% in the cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved the average per person accuracy from 74% to 77% on our free-living dataset (t[353]=7.86, p\u3c0.001). This suggests that future data collections for eating activity detection should also collect detailed ground truth on secondary activities being conducted during eating. Finally, learning from this data collection, we describe a convolutional neural network (CNN) to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts appx 1-5 sec. The novelty of our new approach is that we analyze a much longer window (0.5-15 min) that can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We found that accuracy at detecting eating increased by 15% in longer windows compared to shorter windows. Overall results on CAD were 89% detection of meals with 1.7 false positives for every true positive (FP/TP), and a time weighted accuracy of 80%

Fibre-shaped electronic devices: preparation, characterization and modelling

The study focuses on the development of a parallel coil electrode structure which is favourable for fibre-shaped electronic devices. The experimental results and theoretical modelling demonstrated that this novel electrode structure works well in electrochromic devices and energy harvesting devices.<br /

The Factors Influencing the Behavioural Intention of Overweight Adults to Use Wearable Devices for Sustained Health Monitoring

The volume of wearable devices that can be used for sustained health monitoring purposes is continuously growing within the healthcare sector. These devices allow users to track their own activity levels in real time. However, there are factors that may inhibit the behavioural intention to sustain the use of wearable devices for health monitoring in the long term by overweight adults. These factors include privacy concerns, costs of obtaining wearable devices, theft, frequent charging and short battery life of wearable devices and bulkiness of some wearable devices. It is against this backdrop that this study examined the factors influencing the behavioural intention of overweight adults in South Africa to make use of wearable devices for sustained health monitoring. This research made use of the Expectation Confirmation Model (ECM) as the theoretical foundation of the study. In achieving the aim of this study, a qualitative research approach was used. The purposive sampling technique was selected to identify twenty (20) overweight adults (aged 18-59 years) who are using wearable devices in East London, South Africa. Interviews were conducted with the twenty participants to identify the factors that will influence their behavioural intention to make use of wearable devices to monitor their health. Through thematic analysis, data provided by participants was grouped and summarised into relevant themes to answer the main research question. The study developed a framework that identifies the factors influencing behavioural intention of overweight adults to continue using wearable devices for sustained health monitoring. The factors that were identified include confirmation, perceived usefulness and satisfaction of wearable devices for sustained health monitoring. The realisation of weight loss, monitoring of daily activities and calories through the use of wearable devices was found to positively influence the behavioural intention of the users of wearable devices to continue their usage. However, the major factors that may inhibit the continuous usage of wearable devices for sustained health monitoring are privacy concerns, costs of obtaining wearable devices, theft, frequent charging and short battery life of wearable devices and bulkiness of some wearable devices. Based on the findings, the study recommended the following: (1) the wearable device manufacturers should assure the users of their privacy and confidentiality by providing the needed ii | P a g e interfaces for this purpose; (2) the manufacturers of wearable devices should make the devices less bulky so that they can be more portable; (3) South African government should provide security operatives in isolated areas where people are not feeling secure; (4) the manufacturers of wearable devices make the purchase prices of wearable devices more affordable, especially for low income people; and (5) the manufacturers of wearable devices should improve on battery life and quality of wearable devices so that the devices are more time efficient and require less charging of the devices

The Factors Influencing the Behavioural Intention of Overweight Adults to Use Wearable Devices for Sustained Health Monitoring

The volume of wearable devices that can be used for sustained health monitoring purposes is continuously growing within the healthcare sector. These devices allow users to track their own activity levels in real time. However, there are factors that may inhibit the behavioural intention to sustain the use of wearable devices for health monitoring in the long term by overweight adults. These factors include privacy concerns, costs of obtaining wearable devices, theft, frequent charging and short battery life of wearable devices and bulkiness of some wearable devices. It is against this backdrop that this study examined the factors influencing the behavioural intention of overweight adults in South Africa to make use of wearable devices for sustained health monitoring. This research made use of the Expectation Confirmation Model (ECM) as the theoretical foundation of the study. In achieving the aim of this study, a qualitative research approach was used. The purposive sampling technique was selected to identify twenty (20) overweight adults (aged 18-59 years) who are using wearable devices in East London, South Africa. Interviews were conducted with the twenty participants to identify the factors that will influence their behavioural intention to make use of wearable devices to monitor their health. Through thematic analysis, data provided by participants was grouped and summarised into relevant themes to answer the main research question. The study developed a framework that identifies the factors influencing behavioural intention of overweight adults to continue using wearable devices for sustained health monitoring. The factors that were identified include confirmation, perceived usefulness and satisfaction of wearable devices for sustained health monitoring. The realisation of weight loss, monitoring of daily activities and calories through the use of wearable devices was found to positively influence the behavioural intention of the users of wearable devices to continue their usage. However, the major factors that may inhibit the continuous usage of wearable devices for sustained health monitoring are privacy concerns, costs of obtaining wearable devices, theft, frequent charging and short battery life of wearable devices and bulkiness of some wearable devices. Based on the findings, the study recommended the following: (1) the wearable device manufacturers should assure the users of their privacy and confidentiality by providing the needed ii | P a g e interfaces for this purpose; (2) the manufacturers of wearable devices should make the devices less bulky so that they can be more portable; (3) South African government should provide security operatives in isolated areas where people are not feeling secure; (4) the manufacturers of wearable devices make the purchase prices of wearable devices more affordable, especially for low income people; and (5) the manufacturers of wearable devices should improve on battery life and quality of wearable devices so that the devices are more time efficient and require less charging of the devices