Recent studies shown that elderly persons have a well-defined, regular life routine, organized around their postures, habits, environment, and social relations. Monitoring the daily routine will allow to detect person behavior changes. In this paper, we propose two solutions for detecting changes in the elderly person behavior based on the daily living activities monitoring. For both proposed methods, the person's activity is recorded with a depth sensor to preserve his anonymity. In a first step, the recorded images are classified according to the posture of the person (sitting, standing, lying down, absent and fall) by a modified ResNet-18 model. The first method is a binary decision problem and consists in classifying routine and non routine day according to the postures. Then a behavior index is built using the output of the classifier. The second method consists in describing the routine day by the temporal succession in postures. A similarity between the recorded day and the routine day postures is computed using the minimum edit distance which acts as a behavior index. The number of transformations reflects the change in the person's behavior. Both methods have been tested on a database of depth images recorded in a nursing home over a 85 days period. Inferring the whole dataset gives an accuracy of 89.09% and a F1-score of 85.84% for posture estimation. Using Random Forest, an accuracy of 92% was retrieved for routine/non routine day classification. The two proposed indexes proved their reliability in monitoring behavior changes. The first approach proposes a behavioral index with a median magnitude error less that 0.01 compared to the ground truth. The second one provides a significant difference in the number of transformations between the postures in routine days (Interquartile range 25th-75th=[28, 48]) and non routine days (Interquartile range 25th-75th=[55, 62]).