Anomaly Detection in Smart Homes Based on Kitchen Activities and Machine Learning

Anomaly detection of user behavior in smart homes is vital due to its potential impact on improving the quality of life. Many anomaly detection systems proposed in the literature rely on a wide range of user activities performed at different parts of the smart home environment. This paper presents an anomaly detection framework focused on kitchen activities, a specific area that is not sufficiently addressed by similar studies. The two activities are 'eating' and 'making simple food'. This paper exploits the capability of machine learning algorithms, namely: Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The performance of those algorithms was evaluated and ranged between 80% and 100% for best cases, Demonstrating their high anomaly detection potential despite the relatively small size of the scenario and dataset.