For the technical feasibility assessment, we followed the four-steps approach that was outlined in our proposal: i) Presence: Can physiological signals be reliably measured with wearables during the activity of playing a computer game? ii) Validation: Can physiological signals in this scenario be correlated to an actual mental state like boredom or flow? iii) Classification: Can we automatically classify physiological signals into different states? iv) Action: Can we use such a flow classification system to increase the performance of the players? By re-training and fine-tuning our Emotion AI, we could confirm all these questions, proving technological feasibility of the approach. To assess the economic viability of our approach, we conducted a market research with regard to AI in general and specifically Emotion AI. The market research was executed in the form of an online questionnaire. We surveyed 15 companies of different industries. We specifically asked decision makers about the willingness to pay for AI solutions, possible product packages and possible payment models (e.g. pay as you go, consulting, etc.). There were specific questions about Affective Computing and Emotion AI, which should also stimulate to think about emotion and affect detection technologies like our Emotion AI and its integration in the daily business. Overall the survey showed, that there’s a high interest in Emotion AI technologies just like as in AI technologies in general. (Top-level) managers of large European companies believe in the large potential of AI and see AI-based innovation as a strategically critical factor for Europe to stay competitive with China and the US. Furthermore, TAWNY’s current mode of operation, i.e. providing off-the-shelf Emotion AI optionally tailored to client-specific use cases, fits well with the current way large companies adopt and apply AI (i.e. buying off-the-shelf solutions and custom external development). The flow classification system developed in this feasibility assessment is directly related to the current main drivers for AI adoption, i.e. efficiency, cost-saving, and enabling completely new products. Consequently, we want to take next steps towards further commercialization of our solution. We also want to present the work of this project at tech conferences, trade fairs, etc. to raise awareness for out technology.
