Today, the population of elderly people is dramatically increasing. To help with the problem, smart homes provide technologies and services that can help elderly people to live independently and comfortably in their own homes. One such service in smart homes is the detection of abnormal situations based on individuals' daily routine. This is important as some situations can lead to serious health issues if they have not been detected in the early stage. This paper presents a conceptual model for abnormality detection using case-based reasoning. It utilizes previous cases, which are built from a publicly available smart home dataset. To evaluate the performance, the cases are divided into two case-based sizes which contain seven and fourteen days of monitoring task. To avoid bias, the performance is also measured against two voluntary individuals who have no knowledge of the dataset. The results show that the system is able to detect abnormal situations with the best accuracy of 81.3%.
