Preliminary Assessment of a Low-Sampling-Rate Wearable Head-Mounted Inertial Sensor System for Human Activity Recognition
Mariangela Pinnelli, Moscol Albanil Isabel Del Pilar, Alessandro Dascola, Chiara Romano, Ilaria Condó, Francesca Santucci, Alessandro Ledda, Emiliano Schena, Roberto Setola, Carlo Massaroni · 2025
Human Activity Recognition (HAR) plays a crucial role in occupational safety and worker monitoring, particularly in high-risk environments where real-time activity classification can support risk assessment and injury prevention. Traditionally, HAR systems rely on high-frequency data acquisition, requiring substantial power and computational resources that can limit their integration into wearable safety equipment. This study explores the feasibility of HAR using a lowsampling rate head-mounted inertial sensor system, evaluating its effectiveness in continuous worker activity monitoring without introducing additional burdens or interfering with personal protective equipment (PPE). A wearable system was designed and tested under controlled laboratory conditions with five volunteer workers performing four standard activities: walking, standing, climbing stairs, and running. The accelerometer data were processed using feature extraction in both time and frequency domains, followed by classification with a Random Forest (RF) model. The system achieved an overall accuracy of 95.42% and an $F 1$-score of 0.95, demonstrating that reliable HAR can still be achieved.