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Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping
- Source :
- Journal of NeuroEngineering and Rehabilitation
- Publication Year :
- 2016
- Publisher :
- Carnegie Mellon University, 2016.
-
Abstract
- Background Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object. Methods Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps. Results Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control. Conclusions Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users. Trial registration NCT01364480 and NCT01894802. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0134-9) contains supplementary material, which is available to authorized users.
- Subjects :
- Adult
Male
FOS: Computer and information sciences
Engineering
Neuroprosthetic
media_common.quotation_subject
Movement
0206 medical engineering
Health Informatics
02 engineering and technology
Quadriplegia
Task (project management)
Upper Extremity
03 medical and health sciences
0302 clinical medicine
Shared mode control
Human–computer interaction
Humans
Function (engineering)
media_common
Brain–computer interface
Hand Strength
business.industry
Research
GRASP
Rehabilitation
Neurological Rehabilitation
80101 Adaptive Agents and Intelligent Robotics
Brain
Robotics
Brain-machine interface
Middle Aged
Object (computer science)
Hand
020601 biomedical engineering
body regions
Assistive technology
Brain-computer interface
Control system
Brain-Computer Interfaces
Female
Artificial intelligence
business
Robotic arm
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 01364480
- Database :
- OpenAIRE
- Journal :
- Journal of NeuroEngineering and Rehabilitation
- Accession number :
- edsair.doi.dedup.....f106f511f5929d45a7db50be1eb84d4f
- Full Text :
- https://doi.org/10.1184/r1/6552206