IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2019 · 40 citations · 38 references
EngineeringBiomedical EngineeringRehabilitation MedicineMedical RoboticsProstheticsRoboticsLower-limb ProstheticsNeural ProstheticsProsthetic RoboticsRehabilitation RoboticsUpper-limb ProstheticsVirtual RealityClosed-loop Neuroprosthetic ControlClosed-loop Prosthesis ControlNeural Prosthetic ControlNeuroprosthetic ControlProsthetic ControlProsthetic Limb ControlRobotic ProstheticsUpper-limb Prosthesis Control
Although recent advances in neuroprostheses offer opportunities for improved and intuitive control of advanced motorized and sensorized robotic arms, practical complications associated with such hardware can impede the research necessary for clinical translation. These hurdles potentially can be reduced with virtual reality environments (VREs) with embedded physics engines using virtual models of physical robotic hands. These software suites offer several advantages over physical prototypes, including high repeatability, reduced human error, elimination of many secondary sensory cues, and others. There are limited demonstrations of closed-loop prostheses in the VRE, and it is unclear whether VRE performance translates to the physical world. Here we describe how two trans-radial amputees with neural and intramuscular implants identified objects and performed activities of daily living with closed-loop control of prostheses in the VRE. Our initial evidence further suggests that capabilities with virtual prostheses may be predictors of physical prosthesis performance, demonstrating the utility of VREs for neuroprosthetic research.
38
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, Yuval Tassa · 2012 · 4.4K citations
Engineering, Mechanical Engineering, Robotics Engineering +17
An objective and standardized test of hand function.
R H Jebsen, Nicholas F. Taylor, Roberta B. Trieschmann et al. · PubMed · 1969 · 1.7K citations