Towards Ambient Intelligence-based Environments for Fall Detection and Indoor Localization: Methodology for a Simplistic Design for Real-World Implementation

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. Prior works in these fields have several limitations, such as – lack of real-world testing, lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware or software for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a simplistic design paradigm for the development of an ambient intelligence-based living environment with functionalities to perform indoor localization and fall detection during ADLs. The hardware necessary for the development of this system involves integration of easily available sensors, the combined cost of which is USD 262.15 – which upholds its cost-effectiveness. The results from real-world experiments that were performed uphold the effectiveness of the system design to capture multimodal components of the user interaction data during ADLs that are necessary for the detection of falls as well as indoor localization.