An aging population paired with a general societal shift towards sedentary living has created the need for interventions aimed at monitoring and encouraging physical activity, particularly within in the homes of community dwelling older adults. While numerous technologies have been developed to track activity—ranging from wearable sensors to very dense networks of passive sensors— challenges with respect to usability, obtrusiveness, and cost may prevent widespread adoption and implementation among the elderly. On the other hand, lower-impact solutions (such as systems consisting of a small number of simple, low-cost sensors) may not provide sufficient data to accurately estimate activity levels. This research uses simulation, optimization, and regression analyses to assess the feasibility of using a small number of sensors, placed throughout the home, to track movement and infer physical activity levels of older adults living independently. Based on a standardized dataset of activity (the American Time Use Survey) and actual assisted living apartment layouts, optimal sensor number and placement are determined. The research also identifies preferred approaches for assigning sensors to locations, evaluates the magnitude of errors inherent in the approach, and sheds light on which apartment layouts are best suited for incorporating these innovative and essential healthcare technologies.