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Grasp Performance of a Soft Synergy-Based Prosthetic Hand: A Pilot Study

Alycia Gailey, Sasha Blue Godfrey, Ryan Breighner, Karen L. Andrews, Kristin D. Zhao, Antonio Bicchi, Marco Santello

Year
2017
Citations
11

Abstract

Current prosthetic hands are frequently rejected in part due to limited functionality and versatility. We assessed the feasibility of a novel prosthetic hand, the SoftHand Pro (SHP), whose design combines soft robotics and hand postural synergies. Able-bodied subjects ( ) tracked cursor motion by opening and closing the SHP and performed a grasp-lift-hold-release (GLHR) task with a sensorized cylindrical object of variable weight. The SHP control was driven by electromyographic (EMG) signals from two antagonistic muscles. Although the time to perform the GLHR task was longer for the SHP than native hand for the first few trials (10.2 ± 1.4 s and 2.13 ± 0.09 s, respectively), performance was much faster on subsequent trials (~5 s). The SHP steady-state grip force was significantly modulated as a function of object weight ( ). For the native hand, however, peak and steady-state grip forces were modulated to a greater extent (+68% and +91%, respectively). These changes were mediated by the modulation of EMG amplitude and co-contraction. These data suggest that the SHP has a promise for prosthetic applications and point-to-design modifications that could improve the SHP.

Keywords

GRASPProsthetic handComputer scienceElectromyographyLift (data mining)RoboticsTask (project management)Hand strengthSoft roboticsArtificial intelligence

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