Is healthy eating simply the intake of correct quantities of certain nutrients, regardless of how and where? This project addresses improving wellbeing within the broad context of eating.

Eating-related guidance products have a hyper-focus on nutrition and/or weight loss, to the detriment of a wider definition of “health”. Wellbeing is not only physical health metrics, but has a deeper psychological foundation. Eating experiences should focus on this in order to achieve a truly healthy relationship with food, and an improved level of overall wellbeing. Positive AI builds on the framework of positive design in order to do this, and this project produces a tangible example of how this can be done.

This project focuses on the issue within the specific context of eating at home. The simplified context retains vital nuance, but simplifies the focus. By gaining an understanding of this and how design and AI can be applied in determined ways, actual results can be obtained. The problem is then to promote mindful home-dining experiences by providing a service in which users can feel both in control of what they eat and closer to their loved ones.

The problem is addressed by designing an app-based intervention system - FoodVibe - which harnesses the power of AI to create a highly personalised recipe and experience recommendation system. It facilitates these with the goal of improving wellbeing based on a psychological wellbeing model tailored to the specific context, and learns based on user trends and past experiences. The wellbeing model, based on design analyses of various apps in the broader eating context in combination with wellbeing theories, gives initial direction. This is then thoroughly scrutinised in an evaluation section, leading to future recommendations becoming clear.

In the inland navigation environment, the serious AIS signal congestion and loss result in the ship AIS trajectory to be sparse and discontinuous. Traditional numerical analysis method is used to fit the lost trajectory of ships, and it cannot adapt to the complicated inland waterways with complicated bend, and is prone to cross the land coast. In this paper, a trajectory estimation method for inland river ship was proposed based on navigation experience. Based on the spatial consistency, time consistency and heading consistency, the history similar ship trajectories were extracted for neighbor sampling points of sparse trajectory, and the navigation experience was estimated by the method of Kernel density estimation, and navigational heat distribution map was modelled. And then the optimal route was calculated as approximate estimation trajectory in consideration of ship movement trend based on navigation heat. Three typical inland river channel scenarios were selected to carry out sparse trajectory restoration experiments. Results show that the proposed method can adapt well to the complex terrain of the inland waterway.@en