1 Copyright © 2020 for this paper by its authors. Use permitted under Creative Com- mons License Attribution 4.0 International (CC BY 4.0). Deep Learning and Machine Learning Techniques for Change Detection in Behavior Monitoring Giovanni Diraco, Alessandro Leone, Andrea Caroppo and Pietro Siciliano CNR—National Research Council of Italy, IMM—Institute for Microelectronics and Microsys- tems, Lecce 73010, Italy {giovanni.diraco, alessandro.leone, andrea.caroppo}@cnr.it, pietro.siciliano@le.imm.cnr.it Abstract. Nowadays, smart living environments are equipped with various kinds of sensors which enable enhanced assisted living services. The availabil- ity of huge data volumes coming from heterogeneous sources, together with emerging of novel artificial intelligence methods for data processing and analy- sis, 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-of- the-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 tech- niques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction. Keywords: Change prediction; machine learning; deep learning; ambient as- sisted living; human behavior. 1 Introduction Frail subjects, such as elderly or disabled people, may be at risk when their health conditions are amenable to change, as it is quite common in case of chronic condi- tions. That risk can be reduced by early detecting changes in behavioral and/or physi- cal state, through sensing and assisted living technologies, nowadays available in smart-living environments. Such technologies, indeed, are able to collect huge amounts of data by days, months, and even years, providing important information useful for early detection of changes. Moreover, early change detection makes it pos- sible to alert formal/informal caregivers and health-care personnel in advance when significant changes or anomalies are detected, before critical levels are reached and so preventing chronic diseases. The huge amounts of heterogeneous data collected by different devices require automated analysis; thus there is a growing interest in auto- matic systems for detecting abnormal activities and behaviors in the context of smart