## 6 Discussions of Workshop Results
### WISE-WARE: Intelligent Middleware
A critical aspect of this design is the emphasis on data privacy and security, where all data is stored locally on the user's home device, thereby limiting exposure of personal data without proper anonymisation, encryption, and explicit user consent. By aggregating data from multiple sources and sensors, this approach enables the development of multi-modal intelligent solutions capable of delivering unprecedented preventive care and interventions, ultimately enhancing the quality of life for individuals living with frailty. It is essential to acknowledge the importance of obtaining explicit and informed consent from the users of the system and its connected devices, particularly if they are used in sensitive environments such as bathrooms and bedrooms, where privacy concerns are heightened. To address this, researchers and service providers need to ensure that users provide clear and informed consent before participating in any aspect of these activities. In accordance with GDPR regulations, participants should be fully informed about how their data will be collected, used, and stored, and that they have the right to withdraw consent at  
Figure 14: WISE-WARE middleware architecture. WISE-WARE enables the integration of smart data models for anonymisation, API conversion, and data aggregation and storage. This middleware multi-layer architecture facilitates communication with home and external smart systems, including home IoT devices, smart home platforms, and other smart context brokers. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Features** \\ \hline \multirow{3}{*}{Must} & **(M1)** Give users the ability to _learn_ how to use it based on their own pace and learning style \\  & **(M2)** It is easy to _troubleshoot_ problems to maintain independence \\  & **(S1)** Allow the use of _varied_, _affordable_ devices \\ \multirow{3}{*}{Should} & **(S2)** Allow users to control _data privacy_ and avoid feeling observed \\  & **(S3)** Adapt to individual _needs_ and _preferences_ \\  & **(C1)** Integrate chatbots to provide _personalised tutoring_ \\ \multirow{3}{*}{Could} & **(C2)** Provide _integrated assistance_ (from multiple devices) to help maintain independence \\ \multirow{3}{*}{Won’t} & **(W1)** Use various _control modalities_ as the user’s faculties deteriorate \\ \end{tabular}
\end{table} 
Table 5: MoScOW analysis results for WISE-WARE system requirements. any time. Furthermore, participants should be made aware of their \"right to be forgotten,\" which allows them to request the deletion or the anonymisation of their data at any point during or after the conclusion of these activities. We successfully implemented our intelligent middleware by leveraging a suite of open-source technologies, including Apache Kafka for distributed event streaming, Home Assistant for home automation, and Python v.3.11.2 as the primary programming language. The middleware was deployed on a Raspberry Pi 4 (RPi4) (Cambridge, UK) using Docker v.23.0, an open-source software platform for virtualised applications, to ensure seamless execution and portability. Our implementation was rigorously tested with a diverse array of assistive devices from prominent providers, such as SwitchBot (Beijing, China), Aldebaran Robotics (NAO Robot) (Paris, France), TP-Link (Shenzhen, China), Amazon (Alexa) (Synnyvale, CA, USA), FIBARO (Poznan, Poland), and Philips (Eindhoven, Netherlands), at two state-of-the-art facilities--the Sheffield Hallam University Robotics Lab and the University of Nottingham's Cobot Maker Space. Testing was conducted by the researchers and did not involve other human participants. While the intricacies of the middleware implementation and testing scenarios are beyond the scope of this paper, we have made all developed software, documentation, and installation videos publicly available on our dedicated GitHub (San Francisco, CA, USA) repository, facilitating reproducibility and future collaboration.