Semi-supervised methodologies to tackle the annotated data scarcity problem in the field of HAR

In the field of Human Activity Recognition (HAR) the majority of approaches exploit fully supervised methodologies to process inertial sensor data collected from the users' wearable devices. Unfortunately, those solutions require users to collect a large number of annotated examples to train the recognition model, which is costly, unpractical, and time-consuming. In this paper, we propose diverse semi-supervised methodologies to tackle the data scarcity issue in the field of HAR. In particular, in Caviar and ProCaviar we introduce novel knowledge-based reasoning engines that exploiting the context data (e.g. semantic location, weather condition) allows a statistical classifier trained with a limited number of example to recognise a wide set of activities. Then, we propose FedHAR an hybrid semi-supervised and Federated-learning based system that enables distributing the training of an activity recognition model among a large number of subject, reducing the effort required from users to collect annotated data while preserving their privacy.