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 (A.D.L.s), 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 A.D.L.s 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 A.D.L.s 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 an Ambient Assisted Living system with functionalities to perform indoor localization and fall detection during A.D.L.s. The results from real-world experiments uphold the effectiveness of the system to capture multimodal components of the user interaction data during A.D.L.s that are necessary for performing fall detection and indoor localization. To add to the above, a comparison study is also presented, which shows that the cost for the development of this system is the least as compared to prior works in the fields of fall detection, indoor localization, and assisted living, which involved real-world development, thereby upholding the cost-effective nature of the proposed system.