Leonardo Archetti
Papers
2
Total Citations
15
H-Index
2
About
Leonardo Archetti is a researcher at the forefront of human-robot interaction, specializing in the intersection of machine learning, wearable sensing, and biomechanics. His work centers on decoding human motion and predicting intentions—a critical challenge for advancing robotic rehabilitation and collaborative industrial robots. Archetti’s key contributions lie in developing machine learning techniques that analyze body signals to forecast a user’s intended movement, with a particular focus on reaching tasks. His most cited work, "Intention Prediction and Human Health Condition Detection in Reaching Tasks with Machine Learning Techniques" (2021, 11 citations), demonstrates how these methods can simultaneously assess human health conditions, bridging the gap between intention prediction and clinical diagnostics. In a related study (2020, 4 citations), he systematically compared Linear Discriminant Analysis and Random Forest classifiers for predicting movement intentions using wearable sensors, providing foundational benchmarks for the field. By enabling machines to anticipate human actions—especially in inclusive, assistive contexts—Archetti’s research paves the way for safer, more intuitive human-robot collaboration, with direct implications for rehabilitation robotics and adaptive industrial automation.
Research Focus
Key Achievements
Top Papers
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