Stefania Sozzi

Papers

Total Citations

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About

Stefania Sozzi is a leading researcher in human movement science and rehabilitation engineering, with a primary focus on human activity recognition (HAR) and its clinical applications. Her work bridges the gap between machine learning and motor control, particularly in the context of robotics-assisted rehabilitation. Sozzi’s major contribution lies in systematically comparing machine learning approaches for activity recognition across different experimental conditions, such as cross-subject versus non-cross-subject scenarios. Her most-cited paper, “Systematic Comparison of Machine Learning for Activity Recognition in Cross-Subject vs. Non-Cross-Subject Scenarios: A Preliminary Analysis” (2024), has already garnered 3 citations, highlighting its timely relevance. This work is pivotal for automating the detection and classification of human movements, enabling more adaptive and personalized rehabilitation strategies. Sozzi’s research has direct implications for improving the quality of life for individuals with motor impairments, as it supports the development of intelligent assistive technologies. Her interdisciplinary approach, combining biomechanics, signal processing, and artificial intelligence, positions her as an emerging authority in the field. Through her innovative methodologies, Sozzi is shaping the future of data-driven rehabilitation and human–robot interaction.