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This work makes multiple scientific contributions to the field of IndoorLocalization for Ambient Assisted Living in Smart Homes. First, it presents aBig-Data driven methodology that studies the multimodal components of userinteractions and analyzes the data from Bluetooth Low Energy (BLE) beacons andBLE scanners to detect a user's indoor location in a specific activity-basedzone during Activities of Daily Living. Second, it introduces a contextindependent approach that can interpret the accelerometer and gyroscope datafrom diverse behavioral patterns to detect the zone-based indoor location of auser in any Internet of Things (IoT)-based environment. These two approachesachieved performance accuracies of 81.36% and 81.13%, respectively, when testedon a dataset. Third, it presents a methodology to detect the spatialcoordinates of a user's indoor position that outperforms all similar works inthis field, as per the associated root mean squared error - one of theperformance evaluation metrics in ISO/IEC18305:2016- an international standardfor testing Localization and Tracking Systems. Finally, it presents acomprehensive comparative study that includes Random Forest, Artificial NeuralNetwork, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees,Deep Learning, and Linear Regression, to address the challenge of identifyingthe optimal machine learning approach for Indoor Localization.

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes