5 research outputs found
Flavour Enhanced Food Recommendation
We propose a mechanism to use the features of flavour to enhance the quality
of food recommendations. An empirical method to determine the flavour of food
is incorporated into a recommendation engine based on major gustatory nerves.
Such a system has advantages of suggesting food items that the user is more
likely to enjoy based upon matching with their flavour profile through use of
the taste biological domain knowledge. This preliminary intends to spark more
robust mechanisms by which flavour of food is taken into consideration as a
major feature set into food recommendation systems. Our long term vision is to
integrate this with health factors to recommend healthy and tasty food to users
to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted
Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page
A Food Recommender System in Academic Environments Based on Machine Learning Models
Background: People's health depends on the use of proper diet as an important
factor. Today, with the increasing mechanization of people's lives, proper
eating habits and behaviors are neglected. On the other hand, food
recommendations in the field of health have also tried to deal with this issue.
But with the introduction of the Western nutrition style and the advancement of
Western chemical medicine, many issues have emerged in the field of disease
treatment and nutrition. Recent advances in technology and the use of
artificial intelligence methods in information systems have led to the creation
of recommender systems in order to improve people's health. Methods: A hybrid
recommender system including, collaborative filtering, content-based, and
knowledge-based models was used. Machine learning models such as Decision Tree,
k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field
of food recommender systems on 2519 students in the nutrition management system
of a university. Student information including profile information for basal
metabolic rate, student reservation records, and selected diet type is received
online. Among the 15 features collected and after consulting nutrition experts,
the most effective features are selected through feature engineering. Using
machine learning models based on energy indicators and food selection history
by students, food from the university menu is recommended to students. Results:
The AdaBoost model has the highest performance in terms of accuracy with a rate
of 73.70 percent. Conclusion: Considering the importance of diet in people's
health, recommender systems are effective in obtaining useful information from
a huge amount of data. Keywords: Recommender system, Food behavior and habits,
Machine learning, Classificatio
Continuous Health Interface Event Retrieval
Knowing the state of our health at every moment in time is critical for
advances in health science. Using data obtained outside an episodic clinical
setting is the first step towards building a continuous health estimation
system. In this paper, we explore a system that allows users to combine events
and data streams from different sources to retrieve complex biological events,
such as cardiovascular volume overload. These complex events, which have been
explored in biomedical literature and which we call interface events, have a
direct causal impact on relevant biological systems. They are the interface
through which the lifestyle events influence our health. We retrieve the
interface events from existing events and data streams by encoding domain
knowledge using an event operator language.Comment: ACM International Conference on Multimedia Retrieval 2020 (ICMR
2020), held in Dublin, Ireland from June 8-11, 202
Health State Estimation
Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin