A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

中文翻译：

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多模式传感器数据集成，用于环境辅助生活环境中的室内定位

可靠的室内定位系统（IPS）是环境辅助生活（AAL）概念的关键部分。基于接收信号强度指示（RSSI）映射，使用Wi-Fi指纹识别技术确定用户的位置可以避免部署专用定位基础结构的麻烦，但会带来自身的问题。由于环境变化条件导致的设备异质性和RSSI在空间和时间上的可变性对基于此技术的定位系统提出了挑战。这项研究的主要目的是研究利用其他传感器协助定位系统提供更准确的预测的可行性。特别是，这项工作中提出的实验表明，惯性运动单元（IMU）（默认情况下存在于智能手机或智能手表等智能设备中）可以提高AAL环境中室内定位系统的性能。此外，本文评估了一组基于训练数据以及定位策略（例如数据缩放和使用连续Wi-Fi扫描以进一步提高IPS可靠性）的补充策略来预测定位系统未来性能的技术。预测。这项研究表明，可以从此类策略中得出可靠的定位估计。