Elisa Digo
Papers
10
Total Citations
98
H-Index
4
About
Elisa Digo is a leading researcher at the intersection of human motion analysis and collaborative robotics, with a focus on enhancing human-robot interaction (HRI) in industrial settings. Her work centers on developing real-time, wearable sensing systems—primarily using Inertial Measurement Units (IMUs)—to capture and interpret upper limb kinematics during typical manufacturing and assembly tasks. A key contribution is the fusion of spatial and inertial data to predict human movement, enabling robots to react safely and effectively in shared workspaces. Her highly cited papers, including “Real-time estimation of upper limbs kinematics with IMUs during typical industrial gestures” (29 citations) and “A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios” (26 citations), have established foundational methods for worker motion tracking. Digo has also pioneered the use of robot calibration techniques to model the human arm and applied deep learning to identify abrupt movements, improving safety in collaborative applications. Her work is instrumental in advancing Industry 4.0’s vision of seamless, safe human-robot teamwork, with direct implications for ergonomics, rehabilitation, and smart manufacturing.
Research Focus
Key Achievements
Top Papers
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- 4Using a Robot Calibration Approach Toward Fitting a Human Arm Model5 citations · 2021
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- 6Upper Limbs Motion Tracking for Collaborative Robotic Applications4 citations · 2020
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