During the first project period, we carried out extensive stakeholder engagements to define the end-user requirements and most relevant use scenarios. To that end, we conducted five distinct online surveys targeting athletes, rehabilitation users, and swimming coaches. Furthermore, we performed expert interviews with rehabilitation specialists, summing up to almost 1300 evaluated stakeholder responses. Based on these results, we first defined the specific goals for our targeted edge-AI algorithms, which we concluded to be the following:
1) Land-based scenario: An edge-AI algorithm for predicting the personal performance indicator and metabolic thresholds in real-time during training by analysing the data streams of a multitude of wearable sensors, including also biochemical data delivered by our wearable sweat biosensor. The performance indicator and metabolic thresholds delineate exercise intensity domains and provide quantitative benchmarks for training prescription, and are, therefore, equally relevant both for athletic and rehabilitation use scenarios. However, a valid assessment of these parameters can currently only be obtained in a laboratory environment by invasive methods.
2) Water-based scenario: An edge-AI algorithm that provides live assessment of swimming techniques by real-time analysis of biomechanical parameters delivered by multiple sensors placed at various critical body positions. Here, currently available systems can only record data for post-training analysis, which is a direct result of the non-existence of effective in-water data communication technology. By leveraging the in-water communication technology developed in the project, we target to deliver for the first time live swimming technique assessment, thereby enabling direct response to swimmers and swimming trainers.
With the edge-AI algorithms defined, we next specified distinct demonstrator Setups that will enable us to acquire the needed data under the defined use scenarios. These include two Setups for the land-based scenario, with the first one constituting a basic platform that is mostly comprised of commercially available components, while the second one adds our wearable sweat biosensor, and it also offers a high degree of smart textile integration. Similarly, for the water-based scenario we target two Setup variants, comprising a low-risk basic version relying on in-water data communication via waveguides integrated into a full body swimsuit, while the higher risk advanced version targets in-water data communication by wireless sub-GHz transceivers.
To ease the development of these Setups we specified a list of technologies that are to be integrated into the Setups. These technologies include different wearable sensors (for measuring heart rate, acceleration, muscle activation, respiration rate, blood oxygenation levels, and various biochemical sweat parameters), the central edge-AI processor, in-water communication modules, a gateway between the central processor and the cloud environment, various textile integration techniques, and procedures for data analysis, edge-AI processing and data encryption / security. We initiated the development and technical validation of these technologies within the first project period, and we will finalize their development and integration into our demonstrator Setups during the second project period, including also the end-user validation of our demonstrator Setups.
