Fabiola Spolaor
Papers
5
Total Citations
82
H-Index
4
About
Fabiola Spolaor is a leading researcher at the intersection of rehabilitation robotics, human motion analysis, and neurorehabilitation. Her work focuses on developing intelligent human-robot interfaces that decode patient intent from surface electromyography (sEMG) signals, enabling more responsive and effective assistive devices. In her highly cited 2010 paper (38 citations), she pioneered the use of Support Vector Machine (SVM) classifiers to identify locomotion modes from sEMG, laying critical groundwork for next-generation exoskeletons. She further advanced single-joint angle estimation using Gaussian Mixture Models (GMMs), demonstrating how probabilistic models can translate muscle activity into smooth, online robotic control. Beyond algorithm development, Spolaor has made significant clinical contributions. Her randomized controlled trial (2022, 14 citations) quantitatively assessed the effects of EksoGT® exoskeleton training on gait in Parkinson’s disease patients, showing measurable improvements in stride length and joint range of motion. She has also explored proprioceptive focal stimulation with the Equistasi® device for postural control in Parkinson’s. With a career spanning foundational machine learning for prosthetics to rigorous clinical validation, Spolaor’s work directly bridges engineering innovation and tangible patient outcomes.
Research Focus
Key Achievements
Top Papers
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- 2GMM-Based Single-Joint Angle Estimation Using EMG Signals22 citations · 2015
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