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Health State Estimation

arXiv.org Artificial Intelligence

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.


AI's Next Phase – Thriving Through Implementation - WatersTechnology.com

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Many in financial services are trialing artificial intelligence (AI) applications, with projects increasingly sophisticated in methodology and ambition. WatersTechnology, in partnership with SmartStream, recently convened a Chatham House-style discussion with industry technologists to discuss their hopes for AI as well as the practical and ethical challenges to greater adoption. While the world of AI grows ever wider, one constant has shown through as firms inch closer to implementation--gambits with a strong data orientation will thrive, and those without will not. Banks and asset managers experimenting with AI have come through the early stages with enthusiasm, but, as with the peloton in the Tour de France, they are beginning to feel some strain in their legs. For many, arduous new terrain still awaits before reaching the finish line.


How one company is trying to transform the Tour de France with big data and machine learning - Tech2

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For someone who doesn't watch the Tour de France, the nuances of this multi-stage bicycle race can be quite difficult to grasp. While an outside observer might note that one team is winning and that another is lagging, the why of the matter will be obscured. Dimension Data, the official technology partner for the Tour de France, has some ideas on how to make this data more friendly and accessible to a larger audience. Dimension Data has been working with teams and the Amaury Sport Organisation (ASO), the event organizer, to integrate sensors into the bikes. The data generated from these sensors is used to create what is effectively a real-time map of the circuit, and a lot more besides.


Machine learning comes to Tour de France

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Cycling fans will be able to get deeper insights into one of the most popular races, Tour de France, with the introduction of machine learning algorithm. The race, which began in Düsseldorf, Germany on July 1, finishes at the Champs-Élysées in Paris on July 23. The new data analytics platform combines live and historical race data, and also enables fans to benefit from rider profiles to understand better the environments and circumstances in which riders perform best. At the core of the live tracking and data analytics solution are GPS transponders installed under the saddles of each bike. The data collected from these transponders is combined with external data about the course gradient and prevailing weather conditions to generate insights such as live speed and the location of individual riders, distance between riders, and composition of groups within the race.


Machine Learning Comes to Tour de France - insideBIGDATA

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Amaury Sport Organisation (A.S.O.), organizers of the Tour de France, and Dimension Data, the Official Technology Partner of the Tour de France, announced the introduction of machine learning technologies at this year's Tour de France to give cycling fans across the globe an unprecedented experience of this year's event. The race begins in Düsseldorf on Saturday and finishes at the Champs-Élysées in Paris on 23 July. This year, Dimension Data's data analytics platform, which was developed in partnership with A.S.O., incorporates machine learning and complex algorithms that combine live and historical race data to provide even deeper levels of insight as the race unfolds. Fans will also benefit from rider profiles to understand more about environments and circumstances in which riders perform best. As part of a new pilot this year, A.S.O. and Dimension Data are exploring the role of predictive analytics technologies to assess the likelihood of various race scenarios, such as whether the peloton will catch the breakaway riders at certain stages of the race.


Machine Learning Comes to Tour De France

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LONDON & PARIS--(BUSINESS WIRE)--Amaury Sport Organisation (A.S.O.), organisers of the Tour de France, and Dimension Data, the Official Technology Partner of the Tour de France, today announced the introduction of machine learning technologies at this year's Tour de France to give cycling fans across the globe an unprecedented experience of this year's event. The race begins in Düsseldorf on Saturday and finishes at the Champs-Élysées in Paris on 23 July. This year, Dimension Data's data analytics platform, which was developed in partnership with A.S.O., incorporates machine learning and complex algorithms that combine live and historical race data to provide even deeper levels of insight as the race unfolds. Fans will also benefit from rider profiles to understand more about environments and circumstances in which riders perform best. As part of a new pilot this year, A.S.O. and Dimension Data are exploring the role of predictive analytics technologies to assess the likelihood of various race scenarios, such as whether the peloton will catch the breakaway riders at certain stages of the race.


Dimension Data brings machine learning to the Tour de France - CBR

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Predictive analytics functionality is also going to be tested out for the first time. Dimension Data is helping to bring machine learning to the Tour de France. The world famous bike race, which beings in Düsseldorf on Saturday and finishes at the Champs-Élysées in Paris on the 23rd July, is for the first time having machine learning algorithms applied to it. The Dimension Data analytics platform, which was developed in partnership with A.S.O, the organisers of the Tour de France, applies machine learning and algorithms that combine live and historical race data to provide a deeper level of insight into the race. Fans will be able to see rider profiles and learn more about the environments and circumstances in which they perform best.


DNP Tour de France 2017 to use AI to predict race outcomes

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The 104th edition of the race will see the Tour carry out a pilot machine learning program that will aim to predict the likelihood of various race scenarios. For example, the data could help researchers glean whether the peloton (the main pack of riders) will catch the breakaway riders at certain stages of the race. Using GPS transponders, installed under the saddles of each bike, a whopping 3 billion data points will be collected throughout the 21 stages of the Tour. These insights will be combined with external data (such as the course gradient and weather conditions) to bring viewers a range of breaking stats, including live speed and the location of individual riders, distance between riders, and composition of groups within the race. The hub for this information will be a cloud-based data centre, which will relay stats to broadcasters, allowing them to tell you even more about your favorite teams or riders.


Ducati Corse Races Ahead of the Pack with Accenture and Machine Learning

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Ducati Corse Races Ahead of the Pack with Accenture and Machine Learning Intelligent MotoGP Racing Bikes Build New Aptitude for Optimal Results MOBILE WORLD CONGRESS, BARCELONA; February 27, 2017 – Ducati Corse, the racing department of Ducati Motor Holding, a world leader in sports motorcycle manufacturing, is working with Accenture (NYSE: ACN) to integrate Internet of Things (IoT) and Artificial Intelligence technologies into the testing of its MotoGP racing bikes. Ducati Corse wants to make testing its race bikes faster, cheaper and more effective. Accenture Analytics, already Official Digital Partner to the Ducati Team racing in the MotoGP World Championship, has lately been working with them to create an intelligent testing approach with a bespoke analytics engine. Integrated Machine Learning technologies mean that the more data that enters the system, the more configurations are available for testing with increasingly accurate performance predictions. Data visualization tools designed for an intuitive user experience will allow testing engineers to interact with insights, tweaking them at any point to create a new perspective on configurations and race times.


Information Flow and the Distinction Between Self-Organized and Top-Down Dynamics in Bicycle Pelotons

AAAI Conferences

Information in bicycle pelotons consists of two main types: displayed information that is perceptible to others; and hidden information available to individual riders about their own physical state. Flow (or transfer) of information in pelotons occurs in two basic ways: 1) between cyclists within a peloton, which riders exploit to adjust tactical objectives (“intra-peloton”); 2) from sources outside a peloton as it is fed to riders via radio communication, or from third parties (“extra-peloton”). A conceptual framework is established for information transfer intra-peloton and extra-peloton. Both kinds of information transfer affect peloton complex dynamics. Pelotons exhibit mixed self-organized and top-down dynamics. These can be isolated and examined independently: self-organized dynamics emerge through local physical rules of interaction, and are distinguishable from the top-down dynamics of human competition, decision-making and information transfer. Both intra and extra-peloton information flow affect individual rider positions and the timing of their positional changes, but neither types of peloton information flow fundamentally alter self-organized structures. In addition to two previously identified peloton resources for which riders compete - energy saved by drafting, and near-front positions - information flow is identified as a third peloton resource. Also, building upon previous work on peloton phase-transitions and self-organized group-sorting, identified here is a transition between a team cluster state in which team-mates ride near each other, and a self-organized “fitness” cluster state in which riders of near equal fitness levels gravitate toward each other.