Unobtrusive activity recognition is known to be the most preferred solution for monitoring daily activities of elderly people. In this paper, Scanpath Trend Analysis (STA) is employed for unobtrusive activity recognition of elderly people living alone. Binary sensor data are used and each activity is considered as a sequence of sensor points for this purpose. The real-world longterm fully annotated Aruba open dataset collected by binary sensors is used for the verification of accuracy and the efficacy of the proposed approach. With the STA, the F1-score of 0.758 is obtained, and furthermore, by adding some extra semantic information through an activity transition matrix, it is possible to have F1-score as 0.863. This F1-score is superior to all the related works that use binary sensor data for activity prediction, while computationally the approach presented is advantageous since long periods of training process can be avoided.