Fine-Grained Human Activity Recognition Through Dead-Reckoning and Temporal Convolutional Networks

Nicolò La Porta, Luca Minardi, Michela Papandrea

Abstract: Human Activity Recognition (HAR) represents an important task for many healthcare applications. From the perspective of developing patient-specific solutions, it is clear how the use of artificial intelligence enhances the potential of HAR. The present work settles its roots in the context of early-diagnosis of neurodevelopmental disorders in children (Autism Spectrum Disorder, ASD) and in the evaluation of their motor skills. In this paper, we present an artificial intelligence-based approach for fine-grained HAR which relies on dead-reckoning applied to data collected through inertial measurement units (IMUs). This approach has been applied on a dataset collected through IMU-embedded toys in order to validate its feasibility in the inference of infants’ fine-grained motor skills. The proposed solution’s workflow starts from the estimation of the orientation and position of solid objects through dead- reckoning exploiting Kalman filters and moves to the extraction of informative features, which are then used to feed a Temporal Convolutional Network (TCN). The achieved training average accuracy of 89% highlights how such a non-intrusive approach reaches great performances on HAR tasks, even overcoming the limitations of most of the works already present in literature, based on wearable sensors and/or computer vision techniques. The presented work and achieved results represent a solid base for IoT-based systems aiming at supporting clinicians in the early diagnosis of ASD in children.