  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.
