TAWNY

Project "TAWNY" data sheet

The following table provides information about the project.

 Partnership

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Map

 Project objective

'The number of things oneThe number of things one can do in a connected and digitized world has become so enormous that no-one is able to actively use even a tiny fraction of all those possibilities, oftentimes leading to a technological overload. This situation has led to the rise of products and services which try to be “smart”, i.e., which try to automatically adapt themselves to the needs and preferences of a user. While at the moment, these smart things require explicit user interaction and feedback to be able to adapt and learn, the TAWNY project aims at introducing the so called “emotional intelligence” - typical in human interaction – in smart products, services, and environments. Based on physiological signals like heart rate and skin resistance – measured with off-the-shelf wearables – and the help of machine learning, TAWNY is able to recognize human affects like emotions, stress, or other kinds of mental state. The technology will be offered as an AI-as-a-Service platform, allowing other developers to easily integrate emotional intelligence into their products and freeing them from having to deal with low-level physiological data directly. Out of the numerous psychological models of interest, the widely-known mental state called 'flow' – i.e., being highly productive and content at the same time – has attracted interest in talks with potential clients. Preliminary experiments of TAWNY have suggested that flow might be measurable with TAWNY's approach which would allow for dynamically adapting a workplace or task to keep users in the state of flow – a win-win situation for the employer and employee. In the feasibility study, TAWNY wants to run a series of experiments and develop a prototype to validate and demonstrate the flow measurement technology. Additionally, market research will be conducted to evaluate the economic viability of such a technology. TAWNY, in general, has the potential to become a core component for smart systems across all industries.'