Wearable Tag for Indoor Localization in the Context of Ambient Assisted Living

The ageing population and the increasing demand for personalized and efficient healthcare have led to the implementation of assistive technologies to provide healthcare in home environments. Ambient assisted living (AAL) is an important research area that addresses this need. The accurate localization of individuals within indoor environments and recognition of their daily life activities is a crucial aspect of AAL, where machine learning (ML) algorithms have been widely explored for this purpose. In this paper, we present the development of a wearable sensor node for real-time indoor localization, and the implementation of machine learning algorithms for human activity recognition in the context of AAL. A wearable Tag and four fixed anchors characterized by ultra-wide band technology (UWB) for real-time indoor localization and an inertial measurement unit (IMU) for 3D acceleration acquisition associated with motor activity classification are described and evaluated in this paper. The proposed approach has the potential to improve the provision of personalized interventions and support for elderly or people with chronic diseases using smart sensors and technologies that follow an Internet of Things (IoT) architecture.