Multi-sensing of fragile persons for risk situation detection: devices, methods, challenges

The ageing of the world population has raised ever-increasing demands for measuring physical conditions and assisting the elderly and/or fragile population at their homes. Physiological, motor and even environmental measurements are excellent indicators of their health status. Furthermore, connected wearable technologies in the framework of the Internet of Things allow for risk situations prevention. Recent advances of Artificial Intelligence techniques make possible high-accuracy decision making on multi-sensory data. Nevertheless, to train models and to perform online real-time detection of risk events from heterogeneous taxonomy, robust wearable devices are required. In this paper, existing solutions are reviewed for human sensing for these purposes. Moreover, we present the implementation of a multi-sensor device for the recognition of risk situations with a focus on the data synchronisation.

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