A Novel Torque-Controlled Hand Exoskeleton to Decode Hand Movements Combining Semg and Fingers Kinematics: A Feasibility Study

Eugenio Capotorti; Emilio Trigili; Zach McKinney; Emanuele Peperoni; Filippo Dell'Agnello; Matteo Fantozzi; Andrea Baldoni; Dario Marconi

RA-L 202216 citations

A Novel Torque-Controlled Hand Exoskeleton to Decode Hand Movements Combining Semg and Fingers Kinematics: A Feasibility Study

Eugenio Capotorti, Emilio Trigili, Zach McKinney, Emanuele Peperoni, Filippo Dell'Agnello, Matteo Fantozzi, Andrea Baldoni, Dario Marconi

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Abstract

This study presents a novel torque-controlled hand exoskeleton, named HandeXos-gamma, which uses a series-elastic actuator (SEA)-based architecture to allow a compliant actuation of the hand joints, and an intention decoding algorithm that combines surface electromyography (sEMG) signals with kinematic information from the exoskeleton's encoders. The algorithm was developed offline using data acquired from healthy subjects who performed two grasping movements (lateral and power grasp) under different operating conditions while wearing the exoskeleton. Performance was evaluated for three variants of the algorithm: one using sEMG signals only, another using kinematic data only, and the last combining sEMG and kinematic data. Results indicated that the combination of the two modalities conferred greater algorithm performance than sEMG alone, thus supporting a new paradigm for adaptive robotic hand rehabilitation.

BibTeX

@inproceedings{ral2022_anoveltorquecont,
  title = {A Novel Torque-Controlled Hand Exoskeleton to Decode Hand Movements Combining Semg and Fingers Kinematics: A Feasibility Study},
  author = {Eugenio Capotorti and Emilio Trigili and Zach McKinney and Emanuele Peperoni and Filippo Dell'Agnello and Matteo Fantozzi and Andrea Baldoni and Dario Marconi and Elisa Taglione and Simona Crea and Nicola Vitiello},
  booktitle = {RA-L 2022},
  year = {2022}
}