React to Effects Fast by Learning, Evaluation, and eXtracted InformatiON
REFLEXION will help the European hightech industry to gain a leading position in personalised smart machines by providing significant improvements in quality and stability during early product roll-out. The results will include the ability to react to unforeseen problems or emerging needs in a speedy and cost-effective way by augmenting products with a layer of data sensing and data-analytics to quickly infer ‘missed’ or ‘misunderstood’ end-customer requirements; detecting issues that escape product release testing and product items that need service and maintenance attention. This knowledge is used to further improve the product.
Reflexion will optimise the full end to end product development lifecycle and maintenance process, bringing in analytics to automate and complement expert knowledge, and enabling predictive maintenance on a broader industrial scale and shortening product evolution development iterations.
Yazzoom's involvement in the project concerns all aspects of anomaly detection by machine learning applied to log based and time series data analytics.
The Active@Work project addresses the development and deployment of a web based solution, centered at helping senior workers in their roles within the organisation, providing services to streamline their integration and responsibilities.
Advanced IoT wearable multi-sensors will be provided to monitor each individual health status, the project will study how the compliance of monitoring with (advanced) wearable devices can be improved in order to generate value information about the health status of the user in the working environment. To assure that the Active@Work prototype addresses market needs, end-users will be involved since an early stage of the project execution, overcoming some of the limitations of existing market solution. The two pilots to be deployed will cover heterogeneous organizational processes on various working environments (local and mobile). To achieve that, an extensive and diverse range of users will be included in order to approach the solution to the end-users real needs.
Yazzoom's involvement in the project concerns all aspects of anomaly detection by machine learning applied to Internet of things sensors (Smartwatches / activity trackers, environmental and position sensors)