Sensor-based Abnormal Behavior Detection Using Autoencoder

The population of elderly people is increasing, with the development of an aging society all over the world. As a result, the number of people who need to take care of themselves, such as elderly people living alone or suffering from dementia, is also increasing. Caring for these people requires not only social burdens but also economic costs. A system that manages their behavior is essential to reduce the cost of caring for them. In this study, we propose an abnormal behavior detection model using smart home sensor data to manage elderly people living alone and people with dementia. Previous studies have used probability models such as a hidden Markov model (HMM) or support vector machine (SVM) model. However, the HMM requires a process to estimate values such as the initial probability, or to define states. It is also possible to detect behavior using a classification model such as an SVM, but in this study, we used an autoencoder, which is a representative unsupervised learning model, to obtain a pattern from the behavior data. The autoencoder model can detect abnormal behavior by extracting the characteristics of the normal behavior data. The models used in this study were trained and tested with normal behavior data, showing an accuracy of more than 99%. For abnormal behavior data, a loss of about 10-30% was observed. This model is expected to assist in effectively managing elderly or demented patients and reduce the cost of caring for them.