It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated, and analyzed, they can be used to assist in determining some onsets of anomalies in the health of elderly at various levels of their Movements and Activities of Daily Living (MADL). This paper explores how the framework of the PageRank algorithm can be extended for monitoring the global movement patterns of older adults at their place of residence. Through utilization of an existing dataset, the paper shows how the movement patterns between various rooms can be represented as a directed graph with weighted edges. To demonstrate how PageRank can be utilized, a base graph representing a normal pattern can be defined as what can be used for further anomaly detection (e.g., at some instances of observation the measured movement pattern deviates from what is previously defined as a normal pattern). It is shown how the PageRank algorithm can detect simulated change in the pattern of motion when compared with the base-line normal pattern. This feature can offer a practical approach for detecting anomalies in movement patterns associated with older adults in their own place of residence and in support of aging in place paradigm.