AIMC Topic: Feeding Behavior

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Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a ...

Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients.

Nutrients
Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for diet...

An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.

Nutrients
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making ...

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds.

PloS one
Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs' feeding trails, which are unvegetated winding tracks left after feeding, have been used ...

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.

Aging
The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. However, few studies have identified reliable markers that reflect aging trajectories based on machine le...

Development of swarm behavior in artificial learning agents that adapt to different foraging environments.

PloS one
Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artifici...

The use of machine learning to detect foraging behaviour in whale sharks: a new tool in conservation.

Journal of fish biology
In this study we present the first attempt at modelling the feeding behaviour of whale sharks using a machine learning analytical method. A total of eight sharks were monitored with tri-axial accelerometers and their foraging behaviours were visually...

Artificial intelligence as an analytic approximation to evaluate associations between parental feeding behaviours and excess weight in Colombian preschoolers.

The British journal of nutrition
Parental practices can affect children's weight and BMI and may even be related to a high prevalence of obesity. Therefore, the aim of this study was to evaluate the relationship between parents' practices related to feeding their children and excess...

The acoustic near-field measurement of aye-ayes' biological auditory system utilizing a biomimetic robotic tap-scanning.

Bioinspiration & biomimetics
The aye-aye (Daubentonia madagascariensis) is best known for its unique acoustic-based foraging behavior called 'tap-scanning' or 'percussive foraging'. The tap-scanning is a unique behavior allowing aye-aye to locate small cavities beneath tree bark...

Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks.

TheScientificWorldJournal
The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity am...