Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one's sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

The Clinician Engineer Hub is an international network that brings together the clinical and biomedical engineering fields and provides medical students and clinicians with an exposure to the endless possibilities created by this intertwinement. This event acts as the first Robotic Webinar of the Clinican Engineer Hub series, and will host Dr. Mauro Dragone! Dr. Dragone will give a talk on the use of Internet of Things (IoT) and Robotic technology for Ambient Assisted Living (AAL) applications. He will provide an overview of the OpenAAL project, which has used a combination of robotic telepresence, cloud technologies, virtual reality, and digital twins technology to provide a platform where researchers, industry, and care providers alongside end-users of assisted living services to co-create technology, where time and distance is no longer a barrier – any time, anyplace access. His research focuses on building smart spaces combining sensors, actuators and robots.

There is a widely-accepted need to revise current forms of healthcare provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face specific challenges relating to the fusion of the heterogeneous sensor modalities. We introduce Bayesian models for sensor fusion, which aims to address the challenges of fusion of heterogeneous sensor modalities. Using this approach we are able to identify the modalities that have most utility for each particular activity, and simultaneously identify which features within that activity are most relevant for a given activity. We further show how the two separate tasks of location prediction and activity recognition can be fused into a single model, which allows for simultaneous learning an prediction for both tasks. We analyse the performance of this model on data collected in the SPHERE house, and show its utility. We also compare against some benchmark models which do not have the full structure, and show how the proposed model compares favourably to these methods.