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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

ABSTRACT: Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5?years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

SUBMITTER: Hochberg LR

PROVIDER: S-EPMC3640850 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Project description:BackgroundCervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.MethodsTwo cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.ResultsParticipants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86 and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60 and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%.ConclusionsWe demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.Trial registrationNCT, NCT03385005. Registered September 26, 2017.

Project description:BackgroundThis feasibility study is intended to assess the acceptability of home-based task-specific reach-to-grasp (RTG) training for people with stroke, and to gather data to inform recruitment, retention, and sample size for a definitive randomized controlled trial.Methods/designThis is to be a randomized controlled feasibility trial recruiting 50 individuals with upper-limb motor impairment after stroke. Participants will be recruited after discharge from hospital and up to 12 months post-stroke from hospital stroke services and community therapy-provider services. Participants will be assessed at baseline, and then electronically randomized and allocated to group by minimization, based on the time post-stroke and extent of upper-limb impairment. The intervention group will receive 14 training sessions, each 1 hour long, with a physiotherapist over 6 weeks and will be encouraged to practice independently for 1 hour/day to give a total of 56 hours of training time per participant. Participants allocated to the control group will receive arm therapy in accordance with usual care. Participants will be measured at 7 weeks post-randomization, and followed-up at 3 and 6 months post-randomization. Primary outcome measures for assessment of arm function are the Action Research Arm Test (ARAT) and Wolf Motor Function Test (WMFT). Secondary measures are the Motor Activity Log, Stroke Impact Scale, Carer Strain Index, and health and social care resource use. All assessments will be conducted by a trained assessor blinded to treatment allocation. Recruitment, adherence, withdrawals, adverse events (AEs), and completeness of data will be recorded and reported.DiscussionThis study will determine the acceptability of the intervention, the characteristics of the population recruited, recruitment and retention rates, descriptive statistics of outcomes, and incidence of AEs. It will provide the information needed for planning a definitive trial to test home-based RTG training.Trial registration isrctnISRCTN56716589.

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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

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