Knowledge Infusion for Context-Aware Sensor-Based Human Activity Recognition

Neuro-symbolic AI methods aim at integrating the capabilities of data-driven deep learning solutions with the ones of more traditional symbolic approaches. These techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) research field, even if they could lead to multiple benefits such as improving model interpretability and reducing the amount of labeled data that is necessary to reliably train the model. In this paper, we propose DUSTIN, a novel knowledge infusion approach for sensor-based HAR. DUSTIN concatenates the features automatically extracted by a CNN model from raw sensor data and high-level context data with the ones inferred by a knowledge-based reasoner. In particular, the symbolic features encode common-sense knowledge about the activities which are consistent with the context of the user, and they are infused within the model before the classification layer. We experimentally evaluated DUSTIN on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 26 users. Our results show that DUSTIN outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data and training epochs to reach satisfying recognition rates.