The main objectives of this system are to preserve elderly independence and increase the efficiency of the homecare practices. In the context of ambient assisted living, in a video-based monitoring system for elderly care was proposed. Other vision-based systems use computer vision to detect specifically generated bidimensional codes in order to locate users and devices in an intelligent environment, such as the TRIP location system. Vision-based approaches use either visible light systems or infrared signals, such as the Active Badge Location System, wherein a wearable tag emits an infrared code that is captured by an interconnected network of sensors. There are different approaches and technologies that have been proposed over the years to tackle indoor positioning. Indoor positioning has been used to model users’ behavior in order to detect early risks related to frailty in elders, guide museum visits and coordinate emergency responses. Indoor positioning systems (IPS) are an essential part of any intelligent environment or pervasive computing system. Finally, in Section 7 we draw the conclusions and propose future areas of research. Section 5 contains an explanation of the testing environment and we discuss the results of the experiments in Section 6. In Section 3 we describe the overall architecture of the system and in Section 4 the location prediction algorithm. Section 2 contains an analysis of the state of the art. The rest of the paper is structured as follows. For the location prediction, we present an algorithm based on using neural embeddings to represent the locations of a house and an attention-based mechanism that instead of being applied to the hidden states of the neural network architecture is used to modify those embeddings. For each test campaign, the performance in terms of mean percentage error in the detection of the indoor position was calculated using a smartphone and a smartwatch, and the results have been discussed. A generic home has been equipped with BLE beacon infrastructure, and several tests have been carried out with different configurations in terms of the number and models of beacons in each room. With both devices, the Bluetooth Low Energy (BLE) technology was exploited to obtain indoor positioning information. In this paper, the indoor positioning issue is addressed by considering the performance obtained while using two different kinds of device to estimate the indoor position: a smartphone and a smartwatch. Based on indoor positioning, it is possible to identify where a user is located and to predict his/her future locations based on the recent location history. Elderly care, guidance systems, energy consumption and security are only some of the possible applications of indoor positioning information. Several AAL applications have been developed that have user positioning as their core capability. These solutions benefit from Internet of Things (IoT)-enabling technologies to improve elderly life thanks to the introduction of intelligent, connected devices. A particular focus is the realization of ambient assisted living (AAL) solutions to enable elderly people to live independently for as long as possible, without intrusiveness from others. In such settings, smart environments are expected to play a crucial role for coping with the needs of sustainability, energy distribution, mobility, health and public safety/security. Several real cases already exemplify smart cities, which use the opportunities provided by innovative technologies to improve the lives of their inhabitants. Smart homes and smart buildings are already equipped with a multitude of embedded devices, along with connected sensors and actuators. The advances in hardware and software technologies have led to the adoption of smart-environments in many contexts of our daily lives.
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