Human Activity Recognition using Smartphone Sensors and Beacon-based Indoor Localization for Ambient Assisted Living Systems

The aim of this paper is to present a novel indoor human activity recognition system designated for ambient assisted living environments. To overcome the drawbacks of current solutions in terms of equipment costs and computational complexity, we propose a simple yet robust activity recognition system that combines the output of an activity classification algorithm using smartphone sensors such as accelerometer and gyroscope with a Beacon-based indoor localization predictor to detect more complex activities based on a decision rule system. For the multiclass classification algorithm we employ a ConvLSTM algorithm, while for the positioning system we propose an ensemble based solution combining Multilayer Perceptron with Gradient Boosted Regression and k Nearest Neighbors. Our solution has been tested in a controlled home environment setup achieving an average localization error of 0.4m while for the final activity recognition system we report an accuracy of 91.0% concluding that the proposed solution is accurate enough to be integrated as a monitoring tool facility within ambient assisted living systems.