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282 results for “Exoskeleton”
Bilateral Stand-alone Robotic Motion-Assisted Finger Exoskeleton for Home Rehabilitation
<p><span>This presents a novel exoskeleton robot which can be used at home for rehabilitating the index fingers of stroke-affected patients. This exoskeleton is designed as a one degree-of-freedom four bar mechanism able to guide the human index finger motion. The proposed device is the only lateral, stand-alone mechanism built till date which can carry the weight of the human hand, thus making the user free from wearing it. The design starts by tracing the trajectory of the index finger using ‘Angulus’ software. ‘SALAR’ software is used for dimensional synthesis of four bar mechanism. Using additive manufacturing technology, a prototype of the proposed device is developed. Static force analysis is done to select the most appropriate actuator for producing the required torque to manipulate the fingers effectively. The kinematics of the index finger while performing finger curl exercise is obtained. The proposed linkage mechanism can drive the index fingers of both the hands. Simulation and experimental results proved feasibility and effectiveness of the proposed design to be used for index finger rehabilitation for a wide range of users and applications by making simple minor alterations in the design. Also, the device can be used for rehabilitating the middle fingers together with the index fingers.</span></p>
SSVEP-exoskeleton
<p>This dataset gathers SSVEP-based BCI recordings of 12 subjects operating an upper limb exoskeleton during a shared control task. The exoskeleton is either controlled with a touchless interface detecting hand poses or with SSVEP-based BCI.</p> <p>This dataset is used in the following publications:</p> <ul> <li>Emmanuel K. Kalunga, Sylvain Chevallier, Olivier Rabreau, Eric Monacelli. <em>Hybrid interface : Integrating BCI in Multimodal Human-Machine Interfaces</em>. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2014, Besancon, France.</li> <li>Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthelemy. <em>Data augmentation in Riemannian space for Brain-Computer Interfaces</em>, STAMLINS (ICML workshop), 2015, Lille, France.</li> <li>Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. <a href="http://www.sciencedirect.com/science/article/pii/S0925231216000540"><em>Online SSVEP-based BCI using Riemannian Geometry</em></a>. Neurocomputing, 2016. arXiv research report on arXiv:1501.03227.</li> </ul> <p>It should be indicated that for these publications, the authors excluded the following sessions due to protocol issues (synchronization problem, hardware failure, etc): subject 08 session <em>record-[2013.04.06-16.22.32]</em>, subject 10 sessions <em>record-[2014.02.26-15.10.48]</em> and <em>record-[2014.02.26-15.50.09]</em>, subject 11 sessions <em>record-[2014.02.24-17.56.37]</em> and <em>record-[2014.02.24-18.02.40]</em>.</p> <ol> <li> <p><em>Arm exoskeleton:</em> The exoskeleton used here is the ESTA robotic arm [1]. ESTA is designed to compensate for muscular dystrophy in the shoulder and elbow muscles occurring in several degenerative diseases, which affect the large muscles but spare the wrists and hands motor capacities.</p> </li> <li> <p><em>Touchless interface:</em> Our touchless interface embeds 5 IR-sensors which could set up in different spatial positions, according to the user requirements. The control system relies on a iterative kNN scheme to learn hand poses of each user. The details of the algorithm is provided in [2].</p> </li> <li> <p><em>Steady-state visually evoked potentials:</em> The g.Mobilab+ device is used for recording EEG at 256 Hz on 8 channels. For SSVEP stimulation, flash stimulus technique has been chosen. To avoid limitation imposed by refresh rate of computer screens, a microcontroller is set up to flash stimuli with light emitting diodes (LED) at frequencies F ={13, 17, 21} Hz. The device has been controlled and the LED blinking is precise up to the millisecond. The eight electrodes are placed according to the 10/20 system on Oz, O1, O2, POz, PO3, PO4, PO7 and PO8. The ground was placed on Fz and the reference was located on the right (or left) hear mastoid.</p> </li> </ol> <p>The datasets contains 12 directories, containing recording from 12 male and female subjects aged between 20 and 28 years. Informed consent was obtained from all subjects, each one has signed a form attesting her or his consent. The subject sits in an electric wheelchair, his right upper limb is resting on the exoskeleton. The exoskeleton is functional but is not used during the recording of this experiment.</p> <p>A panel of size 20x30 cm is attached on the left side of the chair, with 3 groups of 4 LEDs blinking at different frequencies. Even if the panel is on the left side, the user could see it without moving its head. The subjects were asked to sit comfortably in the wheelchair and to follow the auditory instructions, they could move and blink freely.</p> <p>A sequence of trials is proposed to the user. A trial begin by an audio cue indicating which LED to focus on, or to focus on a fixation point set at an equal distance from all LEDs for the reject class. A trial lasts 5 seconds and there is a 3 second pause between each trial. The evaluation is conducted during a session consisting of 32 trials, with 8 trials for each frequency (13Hz, 17Hz and 21Hz) and 8 trials for the reject class, i.e. when the subject is not focusing on any specific blinking LED.</p> <p>The recording are saved in GDF format [3], the stimulations code for each class are available as time events. There is between 2 and 5 sessions for each user, recorded on different days, by the same operators, on the same hardware and in the same conditions.</p> <p>To allow direct Python processing, a gzip pickle version of the data are also available as '.pz' file. Sample code to use these file is provided below.</p> <p>The stimulation code used in GDF file are those defined by OpenVibe:</p> <ul> <li>ExperimentStart: 32769, 0x00008001,</li> <li>ExperimentStop: 32770, 0x00008002</li> <li>VisualStimulationStart: 32779, 0x0000800b</li> <li>VisualStimulationStop: 32780, 0x0000800c</li> <li>Label_00: 33024, 0x00008100</li> <li>Label_01: 33025, 0x00008101</li> <li>Label_02: 33026, 0x00008102</li> <li>Label_03: 33027, 0x00008103</li> </ul> <p>The stimulation code are used as follows: <em>ExperimentStart</em> and <em>ExperimentStop</em> indicate the begining and the end of the session. A trial start with a <em>Label_XX</em> stimulation code indicating the class of the example, there is a 3s pause before the audio cue indicating the stimulus to focus. The audio cue onset is indicated by <em>VisualStimulationStart</em>, this is the start of the trial. The end of the trial take place 5s after and is indicated by <em>VisualStimulationStop</em>. <em>Label_00</em> is for resting class, <em>Label_01</em> is for 13Hz stimulation, <em>Label_02</em> is for 21Hz stimulation and <em>Label_03</em> is for 17Hz stimulation.</p> <p>Two example notebooks explain how to <a href="https://gist.github.com/sylvchev/04b0ee8760f30da9d9c9">extract covariance matrices</a> from the dataset based on the stimulation code and how to use a <a href="https://gist.github.com/sylvchev/3d7442bcc4d78b419afa">Riemannian-based classification</a> scheme with the extracted spatial covariance matrices. It is also possible to rely on <a href="https://gist.github.com/sylvchev/0b8f37252aca85f41f0d">MNE to open and process</a> this dataset.</p> <p>[1] M. Baklouti, P. A. Guyot, E. Monacelli, and S. Couvet. <em>Force controlled upper-limb powered exoskeleton for rehabilitation,</em> in Intelligent Robots and Systems (IROS), 2008, p. 4202.</p> <p>[2] H. Martin, S. Chevallier, and E. Monacelli, <em>Fast calibration of hand movement-based interface for arm exoskeleton control,</em> in European Symposium on Artificial Neural Networks (ESANN), 2012, pp. 573– 578.</p> <p>[3] A. Schlogl, <em>GDF - A general dataformat for biosignals</em>, <a href="http://arxiv.org/abs/cs/0608052">http://arxiv.org/abs/cs/0608052</a></p>
The WISE Trial - Walking Improvement for SCI With Exoskeleton
ClinicalTrials.gov study NCT02943915. IPD Sharing: NO. Countries: 1. Publications: 6.
High Intensity Training for Neurological Injury Using Overground Exoskeletons in Inpatient Rehabilitation
ClinicalTrials.gov study NCT04973852. IPD Sharing: NO. Countries: 1. Publications: 9.
Effect of the Atalante Exoskeleton on Standing and Walking in Individuals With Paraplegia: A Prospective Controlled-Environment Study in a Rehabilitation Context
ClinicalTrials.gov study NCT06941896. IPD Sharing: NO. Countries: 1. Publications: 0.
Chairless Chair Exoskeleton. Work-physiological-biomechanical Analysis of the Lower Extremities
ClinicalTrials.gov study NCT03134144. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Optimization of Hip-exoskeleton Weight Attributes
ClinicalTrials.gov study NCT05120115. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Wearable Lower Extremity Exoskeleton to Promote Walking in Persons With Multiple Sclerosis
ClinicalTrials.gov study NCT02519244. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Powered Exoskeletons in Persons With SCI
ClinicalTrials.gov study NCT02658656. IPD Sharing: NO. Countries: 1. Publications: 1.
Exoskeleton Treatment of Deconditioning Due to Limited Ambulation Caused by Illness or Injury
ClinicalTrials.gov study NCT03355755. IPD Sharing: NO. Countries: 1. Publications: 5.
Robotic Exoskeleton Gait Training for Children With Cerebral Palsy
ClinicalTrials.gov study NCT07049523. IPD Sharing: NO. Countries: 0. Publications: 1.
Improving Gait Performance in Individuals With Spinal Cord Injuries: an Intervention Using Robotic Exoskeletons
ClinicalTrials.gov study NCT03011099. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Restoring Walking With a Powered Exoskeleton After Spinal Cord Injury
ClinicalTrials.gov study NCT02322125. IPD Sharing: NO. Countries: 1. Publications: 2.
Exoskeleton Robotic Assisted Gait Training in Spastic Stroke Post Botulinum Toxin A Injection
ClinicalTrials.gov study NCT06070987. IPD Sharing: NO. Countries: 1. Publications: 63.
Brain Machine Interface Control of an Robotic Exoskeleton in Training Upper Extremity Functions in Stroke
ClinicalTrials.gov study NCT01948739. IPD Sharing: NO. Countries: 1. Publications: 5.
Exoskeleton Training for Spinal Cord Injury Neuropathic Pain (ExSCIP)
ClinicalTrials.gov study NCT06463418. IPD Sharing: YES. Countries: 1. Publications: 41.
Optimizing Ankle Exoskeleton Assistance for Walking Across the Life Span
ClinicalTrials.gov study NCT04033146. IPD Sharing: NO. Countries: 1. Publications: 35.
Work Physiological-Biomechanical Analysis of a Passive Exoskeleton to Support Occupational Lifting and Flexing Processes
ClinicalTrials.gov study NCT03725982. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessing the Efficacy of Passive Exoskeletons for Construction Work: Lab-Based Study
ClinicalTrials.gov study NCT04567797. IPD Sharing: NO. Countries: 1. Publications: 16.
Soft tissue can absorb surprising amounts of energy during knee exoskeleton use
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