Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

Abstract

Cognitive impairment is one of the crucial problems elderly people face. Tracking their daily life activities and detecting early indicators of cognitive decline would be necessary for further diagnosis. Depending on the decline magnitude, monitoring may need to be done over long periods of time to detect abnormal behaviour. In the absence of training data, it would be helpful to learn the normal behaviour and daily life patterns of a (cognitively) healthy person and use them as a basis for tracking other patients. In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. We present a method for generating synthetic data to reflect on some behavior of people with dementia. An RAE model is trained on data of a healthy person in a source household. Then, the resulting RAE is used to detect abnormal behavior in a target house. To evaluate the proposed approach, we compare the results with the-state-of-the-art supervised methods. The results indicate that transfer learning is promising when there is lack of training data.