Deep learning and machine learning techniques for change detection in behavior monitoring

Nowadays, smart living environments are equipped with various kinds of sensors which enable enhanced assisted living services. The availability of huge data volumes coming from heterogeneous sources, together with emerging of novel artificial intelligence methods for data processing and analysis, yields a wide range of actionable insights with the aim to help older adults to live independently with minimal supervision and/or support from others. In this scenario, there is a growing demand for technological solutions to monitor human activities and physiological parameters in order to early detect abnormal conditions and unusual behaviors. The aim of this study is to compare state-ofthe-art machine learning and deep learning approaches suitable for detecting early changes in human behavior. At this purpose, specific synthetic datasets are generated, which include activities of daily living, home locations and vital signs. The achieved results demonstrate the superiority of deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction.