Indoor Positioning and Prediction in Smart Elderly Care: Model, System and Applications

The indoor positioning and prediction technologies can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone, with the aim to predict and prevent the occurrence of emergency or health risks. In this paper, we design and implement an indoor positioning and prediction system, and apply it to smart elderly care. The system consists of two parts: hardware module and software module. The hardware module is responsible for sensing and uploading the location information of the elderly living in the room, while the software module is responsible for receiving and processing the sensing data, modeling and predicting the indoor position of the elderly based on the sensing data, so as to help find the abnormal patterns of the elderly. We also introduce a positioning prediction model that is suitable for processing IoT sensing data. With the model, one can have a more accurate and comprehensive understanding of the regularity and periodicity of the positioning time series for the elderly. To demonstrate the effectiveness and performance gains of our solution, we deploy the system in real world and setup experiments based on the indoor trajectory data collected by sensors. Extensive experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy.