Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

Abstract

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.

Design of Multimedia Vocal Music Education Data Integration System Based on Adaptive Genetic Algorithm

“…When the database receives the information, it must first complete the data conversion and transfer it in an XML document. In this process, it is necessary to clarify the information of the minimum synchronization set [15]. e source of this data information is relatively complicated, and it needs to be confirmed to be correct before it can be arranged in a certain order.…”

Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting

“…It has been suggested that we cannot rely on RSSI alone for indoor localisation in home environments for PD subjects due to shadowing rooms with tight separation [32,35,39]. Sansano et al combine RSSI signals and inertial measurement unit (IMU) data to test the viability of leveraging other sensors in aiding the positioning system to produce a more accurate location estimate [39].…”

“…It has been suggested that we cannot rely on RSSI alone for indoor localisation in home environments for PD subjects due to shadowing rooms with tight separation [32,35,39]. Sansano et al combine RSSI signals and inertial measurement unit (IMU) data to test the viability of leveraging other sensors in aiding the positioning system to produce a more accurate location estimate [39]. Classic machine learning approaches such as Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbour (k-NN) are tested, and the result shows that the RF outperforms other methods in tracking a person in indoor environments.…”

“…concatenation, CNN layer, transformer, self-attention) [28,51]. Our work is inspired by Sansano-Sansano et al [39] where we only utilise accelerometer data to enrich the RSSI, instead of utilising all IMU sensors, in order to reduce battery consumption. In addition, unlike Sansano-Sansano et al who stop at predicting room locations, we go a step further and use room-to-room transition behaviours, as features for a binary classifier predicting whether people with PD are taking their medications or withholding them.…”

“…To perform indoor localisation in home environments, IoT-based platforms with sensors capturing various modalities of data combined with machine learning can be used to provide an unobtrusive and continuous localisation [39]. Typically, many of these techniques take advantage of the radio-frequency signals, the Received Signal Strength Indication (RSSI), emitted by wearables and measured at access points (AP) throughout a home.…”