Emilia Scalona

Papers

Total Citations

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About

Emilia Scalona is a leading researcher in rehabilitation robotics and biomechanics, with a primary focus on improving mobility for individuals with neurological disorders. Her work centers on developing intelligent, data-driven methods for gait analysis and robotic-assisted therapy, particularly for children with cerebral palsy. Her most-cited paper, "Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy" (2015, 74 citations), tackles a critical bottleneck in rehabilitation technology: the need for time-consuming, subject-specific calibration. By validating an inter-subject training procedure for Hidden Markov Models (HMMs), Scalona demonstrated that gait-phase detection—essential for controlling lower-limb robotic exoskeletons—can be achieved without per-patient training, dramatically accelerating clinical deployment. This contribution has been foundational for making robotic rehabilitation more practical and accessible. Beyond this, Scalona’s broader research explores sensor fusion, machine learning, and wearable technology to decode human movement, with an emphasis on translating lab innovations into real-world clinical tools. Her work has been recognized for bridging computational modeling with patient-centered outcomes, making her a key figure in the intersection of artificial intelligence and neurorehabilitation.