The number of older adults and their proportion within the general population are expected to increase in the upcoming decades. Decision makers have turned to technology in order to provide cost-mitigating solutions. Assistive systems have been developed for many of the challenges that can be addressed through technology. However, we find most of them to have singular focus, using proprietary equipment and communication protocols which makes them difficult to integrate into a comprehensive system. Our work was carried out in the development phase of a cyber-physical platform that enables seamless integration of third party vendor devices and applications into a configurable, extensible and cost-effective technological platform for elderly care. The present paper targets the integration of commercial off the shelf components into a real-time indoor localization system, which allows monitoring the older adult’s level of activity and detecting potentially dangerous situations automatically. We present the initial results of our comparative evaluation on the accuracy of localisation and heart rate monitoring of persons within an indoor setting. We briefly present the integrated cyber-physical system developed as part of the project. We use intelligent luminaires that act as location beacons and detail the results of an experiment where we evaluated the performance of two different luminaire types. While our previous experiments have shown that room-level localisation of a moving subject was possible using custom-developed luminaires, we extended our evaluation to cover a cost-effective, commercially available alternative. Real-time location data was combined with heart rate information recorded using a commercially available smartwatch in order to obtain a more complete picture of the monitored person’s level of activity. Our initial results showed that existing technologies can be configured and integrated into a more complex platform, which remains customizable according to each end user’s needs.

In the wake of the Covid-19 pandemic, Ambient Assisted Living (AAL) and Home Monitoring System (HMS) frameworks came into focus as an alternative to classic healthcare services for the ageing world population. Both overwhelmed healthcare systems and the susceptibility of older adults to develop severe complications are indicators that in the near future there will be a shift to a decentralized monitoring paradigm for chronic disease patients.This paper introduces the SMARTCARE system, a solution that integrates both AAL and HMS features in order to provide maximum flexibility in defining monitoring scenarios through the use of IoT devices. More, the system tackles the lack of uniformity that exists in standardization across the IoT field through the use of a modular architecture.

This paper presents an experimental evaluation on the accuracy of indoor localisation. The research was carried out as part of a European Union project targeting the creation of ICT solutions for older adult care. Current expectation is that advances in technology will supplement the human workforce required for older adult care, improve their quality of life and decrease healthcare expenditure. The proposed approach is implemented in the form of a configurable cyber-physical system that enables indoor localization and monitoring of older adults living at home or in residential buildings.
Hardware consists of custom developed luminaires with sensing, communication and processing capabilities. They replace the existing lighting infrastructure, do not look out of place and are cost effective. The luminaires record the strength of a Bluetooth signal emitted by a wearable device equipped by the monitored user. The system’s software server uses trilateration to calculate the person’s location based on known luminaire placement and recorded signal strengths. However, multipath fading caused by the presence of walls, furniture and other objects introduces localisation errors. Our previous experiments showed that room-level accuracy can be achieved using software-based filtering for a stationary subject. Our current objective is to assess system accuracy in the context of a moving subject, and ascertain whether room-level localization is feasible in real time.