Solutions for home automation and assisted living require a lot of manual configuration and/or programming from the users. This calls for greater intelligence with increasing programmability, systems that learn. Knowledge as a Service for Assisted Living in Smart City (KaaS_SCL) provides smart, personalised assistance to individuals indoors and outdoors based on the user profile as well as predictions on health status, traffic, weather, pollution, etc. KaaS_SCL offers a combination of services:
- Automated indoor environment adaptation with functionality for learning user patterns to forecast user desires regarding indoor environment/home appliances configuration and proactively take actions/offer recommendations.
- Remote Health Monitoring and Forecasting comprising functionality for learning patterns in user physical status to identify any abnormality in usual patterns. Family members and/or professional caretakers can be informed and appropriate alarms may be raised if necessary.
- Smart city life providing navigation instructions, information on dangerous locations in the proximity, public transportation help considering user preferences and health/well-being status and a city dashboard.
The aim of the experiment was to perform experiment-based validation of KaaS_SCL based on an existing prototype implementation (done in the H2020 EU Japan project iKaaS). Experimentation is a vital step as it enables the provision of critical insight on the performance of the KaaS_SCL components, to allow for its further exploitation and commercialisation.
Our approach included: (a) Specification of experiment scenarios, validation/performance metrics; (b) Set-up of the KaaS_SCL experimentation framework through the integration of the corresponding prototype with FIESTA-IoT facilities; (c) Experiments, results-analysis (including user experience) and refinements; (d) Promotion of the validated KaaS_SCL experimentation framework and of the FIESTA-IoT platform, through dissemination and demonstrations activities.