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In Malaysia, the elderly population continues to grow. At the same time, young adults are unable to take care of their elderly parents due to work commitments. This results in an increasing number of elderly people living in solitude. Therefore, it is crucial to monitor elderly people’s behavior, especially the pattern of their daily home activities. Abnormal behaviors in carrying out home activities may indicate health concerns in elderly people. Past studies have proposed the use of complex machine learning algorithms to detect anomalies in daily sequences of home activities. In this paper, a simple, alternative method for detecting anomalies in daily sequences of home activities is presented. The experiment results demonstrate that the model achieved a test accuracy of 90.79% on a public dataset.