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This paper addresses the challenging problem of disorientation of elderly people living at home. In order to detect confusion, we monitor the behaviour of the elderly and identify actions that appear alarming in a sensorized and video-controlled smart environment. In the past, our research has focused on identifying situations, activities and interactions between various actors based on user-understandable models. This work addresses the development of a simulation tool capable of synthesizing sensor data and low-level/medium-level scene events. The tool is of great interest with regard to the design and configuration of an elderly disorientation recognition system because it reduces the laborious and expensive need for experimentation with real devices. We integrate this proposal in a comprehensive framework that distinguishes between a recognition line and a simulation line in a potentially continuous and closed cycle. The recognition line goes from a multisensory monitored scene to its semantic interpretation, which could be completed even with only the narration of the facts. In the opposite direction, the simulation line goes from the narration of a scene to its synthesis with the same semantic content into a 3D simulation and the corresponding sensor signals and low/medium events at specific location points.

Abstract. This article presents a preliminary study in order to explore the possibilities for robot localization using measured received signal strength indicator (RSSI) from Bluetooth low energy (BLE) beacons. BLE is a new brand technology focused on information transmission using very low energy consumption. It is being included in mobile devices from year 2011, nowadays almost every new mobile phone is shipped with this technology. Robot localization using particles filter has been developed in recent years using wireless technologies with a significant success. BLE beacons measures are rather noisier than measures from similar wireless devices. In this work we make an initial model of BLE measures and their noise. The model is used to generate data to be processed by a particle filter designed for localization using only ultrawide band (UWB) beacons ranges. Data are generated with different noise level in order to explore localization errors behavior, these levels cover real noise levels founded in RSSI measure characterization.

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