Activity recognition on handheld devices for pedestrian indoor navigation

We propose an inertial sensor-based approach to activity recognition for pedestrian indoor navigation. In the considered scenario a mobile device is held in a hand in front of the user. The recognized activities are the ones relevant to positioning in multi-floor buildings: walking and going up or down the stairs. To model the time dependency between consecutive activities we employ a Hidden Markov Model (HMM). For efficient quantization of continuous features, we apply a random forest classifier. For verification of the proposed algorithm, we conducted experiments with 12 participants and 4 different mobile devices. In our comparison to state-of-the-art approaches, we implement and evaluate major classification algorithms, such as nearest neighbour, decision tree and dynamic Bayesian Network. In the experiments we show the trade-off between computational complexity and classification performance. Furthermore, we demonstrate that the complexity of the HMM can be significantly reduced by replacing it with a dynamic Bayesian network with negligible impact on classification performance. The best of our proposed classifier achieves a classification accuracy of 91% for new users, which offers a 30% improvement compared to state-of-the-art approaches.