Abstract
In the past years, there has been a flourishing of platforms dedicated to Active Assisted Living (AAL) and Active and Healthy Ageing (AHA). Most of them feature as their core elements intelligent systems for the analysis of multisource and multimodal data coming from sensors of various nature inserted in suitable IoT ecosystems. While progress in signal processing and artificial intelligence has shown how these platforms may have a great potential in improving the daylife of seniors or frail subjects, there are still several technological and non-technological barriers that should be torn down before full uptake of the existing solutions. In this paper, we address specifically this issue describing the outcome and creation process of a methodology aimed at evaluating the successful uptake of existing platforms in the field of AHA. We propose a pathway (as part of an overarching methodology) to define and select for Key Performance Indicators (KPIs), taking into account an extensive amount of parameters related to success, uptake and evolution of platforms. For this, we contribute a detailed analysis structured along with the 4 main actions of mapping, observing, understanding, and defining. Our analysis focuses on Platforms, defined as operating environments, under which various applications, agents and intelligent services are designed, implemented, tested, released and maintained. By following the proposed pathway, we were able to define a practical and effective methodology for monitoring and evaluating the uptake and other success indicators of AHA platforms. Besides, by the same token, we were able to provide guidelines and best practices for the development of the next-generation platforms in the AHA domain.
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The work developed under this article was co-funded by the project PlatformUptake.eu, under the European Union’s Horizon H2020 Research and Innovation Program under the Grant Agreement n.875452.
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Carboni, A. et al. (2021). Success and Hindrance Factors of AHA-Oriented Open Service Platforms. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_53
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