Dataset Information

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

ABSTRACT: 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.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.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.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.NCT01364480 and NCT01894802 .

SUBMITTER: Downey JE

PROVIDER: S-EPMC4797113 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

Downey John E JE Weiss Jeffrey M JM Muelling Katharina K Venkatraman Arun A Valois Jean-Sebastien JS Hebert Martial M Bagnell J Andrew JA Schwartz Andrew B AB Collinger Jennifer L JL

Journal of neuroengineering and rehabilitation 20160318

<h4>Background</h4>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 positio ...[more]

PMID: 26987662

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Project description:BackgroundThe aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator.MethodsTen impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability.ResultsAll participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator).ConclusionsAlthough the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.

Project description:Children begin to develop self-awareness when they associate images and abilities with themselves. Such "construction of self" continues throughout adult life as we constantly cycle through different forms of self-awareness, seeking, to redefine ourselves. Modern technologies like screens and artificial intelligence threaten to alter our development of self-awareness, because children and adults are exposed to machines, tele-presences, and displays that increasingly become part of human identity. We use avatars, invent digital lives, and augment ourselves with digital imprints that depart from reality, making the development of self-identification adjust to digital technologies that blur the boundary between us and our devices. To empower children and adults to see themselves and artificially intelligent machines as separately aware entities, we created the persona of a salvaged supermarket security camera refurbished and enhanced with the power of computer vision to detect human faces, and project them on a large-scale 3D face sculpture. The surveillance camera system moves its head to point to human faces at times, but at other times, humans have to get its attention by moving to its vicinity, creating a dynamic where audiences attempt to see their own faces on the sculpture by gazing into the machine's eye. We found that audiences began attaining an understanding of machines that interpret our faces as separate from our identities, with their own agendas and agencies that show by the way they serendipitously interact with us. The machine-projected images of us are their own interpretation rather than our own, distancing us from our digital analogs. In the accompanying workshop, participants learn about how computer vision works by putting on disguises in order to escape from an algorithm detecting them as the same person by analyzing their faces. Participants learn that their own agency affects how machines interpret them, gaining an appreciation for the way their own identities and machines' awareness of them can be separate entities that can be manipulated for play. Together the installation and workshop empower children and adults to think beyond identification with digital technology to recognize the machine's own interpretive abilities that lie separate from human being's own self-awareness.

Dataset Information

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

Publications

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

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