Publications
384 results found
Lambercy O, Dovat L, Yun H, et al., 2011, Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study, JOURNAL OF NEUROENGINEERING AND REHABILITATION, Vol: 8, ISSN: 1743-0003
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- Citations: 85
Kadiallah A, Liaw G, Kawato M, et al., 2011, Impedance control is selectively tuned to multiple directions of movement, JOURNAL OF NEUROPHYSIOLOGY, Vol: 106, Pages: 2737-2748, ISSN: 0022-3077
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- Citations: 23
Ueda J, Burdet E, Gennisson J-L, et al., 2011, Introduction to the Focused Section on Sensing Technologies for Biomechatronics, IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol: 16, Pages: 793-798, ISSN: 1083-4435
Yang C, Ganesh G, Haddadin S, et al., 2011, Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions, IEEE TRANSACTIONS ON ROBOTICS, Vol: 27, Pages: 918-930, ISSN: 1552-3098
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- Citations: 248
Kodl J, Ganesh G, Burdet E, 2011, The CNS Stochastically Selects Motor Plan Utilizing Extrinsic and Intrinsic Representations, PLOS ONE, Vol: 6, ISSN: 1932-6203
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- Citations: 15
Su ELM, Ganesh G, Yeong CF, et al., 2011, Effect of Grip Force and Training in Unstable Dynamics on Micromanipulation Accuracy, IEEE TRANSACTIONS ON HAPTICS, Vol: 4, Pages: 167-174, ISSN: 1939-1412
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- Citations: 11
Campolo D, Widjaja F, Esmaeili M, et al., 2011, Pointing with the wrist: a postural model for Donders' law, EXPERIMENTAL BRAIN RESEARCH, Vol: 212, Pages: 417-427, ISSN: 0014-4819
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- Citations: 16
Melendez-Calderon A, Masia L, Gassert R, et al., 2011, Force Field Adaptation Can Be Learned Using Vision in the Absence of Proprioceptive Error, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 19, Pages: 298-306, ISSN: 1534-4320
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- Citations: 30
Zhou SH, Oetomo D, Tan Y, et al., 2011, Human motor learning through iterativemodel reference adaptive control, Pages: 2883-2888, ISSN: 1474-6670
A computational model using mechanical impedance control in combination with an iterative model reference adaptive control is proposed to capture the hypothesised mechanisms in human motor control and the learning capacity that humans exhibit in adapting their movements to new and unstructured environments. The model uses an iterative learning control law to model human learning through repetitive processes. In this proposed framework, motion command is carried out without the need for inverse kinematics. Learning is performed without an explicit internal model of the body or of the environment. The resulting framework is simulated and compared to the experimental data, involving subjects performing a task in the face of specified disturbance forces. In this paper, a stable task is specifically addressed as the example. It is shown that the proposed framework produces a stable and accurate description of the general behaviour observed in the human motor adaptation. © 2011 IFAC.
Lambercy O, Dovat L, Salman B, et al., 2011, Robot-Assisted Rehabilitation of Hand Function After Stroke with the HapticKnob and the HandCARE, BIOMECHATRONICS IN MEDICINE AND HEALTH CARE, Publisher: PAN STANFORD PUBLISHING PTE LTD, Pages: 43-59, ISBN: 978-981-4241-61-8
Yang C, Burdet E, 2011, A Model of Reference Trajectory Adaptation for Interaction with Objects of Arbitrary Shape and Impedance, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 4121-4126, ISSN: 2153-0858
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- Citations: 13
Gowrishankar G, Osu R, Yoshioka T, et al., 2011, Symbiosis of motor interaction, NEUROSCIENCE RESEARCH, Vol: 71, Pages: E254-E254, ISSN: 0168-0102
Klein J, Chen A, Burdet E, 2011, Instrumented sorting block box for children, a preliminary experiment, IEEE International Conference on Rehabilitation Robotics (ICORR)/International Neurorehabilitation Symposium (INRS)/International Conference on Virtual Rehabilitation (ICVR), Publisher: IEEE, ISSN: 1945-7898
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- Citations: 1
Melendez-Calderon A, Komisar V, Ganesh G, et al., 2011, Classification of strategies for disturbance attenuation in human-human collaborative tasks, 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 2364-2367, ISSN: 1557-170X
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- Citations: 10
Melendez-Calderon A, Bagutti L, Pedrono B, et al., 2011, Hi5: a versatile dual-wrist device to study human-human interaction and bimanual control, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, ISSN: 2153-0858
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- Citations: 1
Sanguineti V, Popovic D, Colombo R, et al., 2010, HUman behavioral modeling for enhancing learning by optimizing human-robot interaction (HUMOUR): Concept and preliminary results
Lambercy O, Dovat L, Yun H, et al., 2010, Robotic assessment of hand function with the HapticKnob
This paper investigates the assessment of hand function after stroke using the HapticKnob, an end-effector based robotic device to train grasping and forearm pronation/supination. A method to extract meaningful parameters to evaluate hand function from kinematic data recorded by the robot during rehabilitation exercises is presented. Step-wise regression analysis has been performed in an attempt to reconstruct clinical assessment scores from the kinematic data collected during a 6-week rehabilitation therapy with the HapticKnob. Good correlations between clinical and reconstructed scores (r=0.669 for Fugl-Meyer Assessment, r=0.689 for Motricity Index, r=0.599 for Motor Assessment Scale, and r=0.792 for Modified Ashworth Scale) illustrate the potential of these objective measures to quantitatively evaluate hand motor function. This offers new possibilities to monitor patients' progress and customize exercise challenge during rehabilitation therapy. © 2010 START Centre.
Rebsamen B, Guan C, Zhang H, et al., 2010, A Brain Controlled Wheelchair to Navigate in Familiar Environments, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 18, Pages: 590-598, ISSN: 1534-4320
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- Citations: 257
Balasubramanian S, Klein J, Burdet E, 2010, Robot-assisted rehabilitation of hand function, CURRENT OPINION IN NEUROLOGY, Vol: 23, Pages: 661-670, ISSN: 1350-7540
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- Citations: 193
Lemmin T, Ganesh G, Gassert R, et al., 2010, Model-based attenuation of movement artifacts in fMRI, JOURNAL OF NEUROSCIENCE METHODS, Vol: 192, Pages: 58-69, ISSN: 0165-0270
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- Citations: 6
Kazemi H, Rappel JK, Poston T, et al., 2010, ASSESSING SUTURING TECHNIQUES USING A VIRTUAL REALITY SURGICAL SIMULATOR, MICROSURGERY, Vol: 30, Pages: 479-486, ISSN: 0738-1085
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- Citations: 29
Ganesh G, Albu-Schäffer A, Haruno M, et al., 2010, Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks, Pages: 2705-2711, ISSN: 1050-4729
Interaction of a robot with dynamic environments would require continuous adaptation of force and impedance, which is generally not available in current robot systems. In contrast, humans learn novel task dynamics with appropriate force and impedance through the concurrent minimization of error and energy, and exhibit the ability to modify movement trajectory to comply with obstacles and minimize forces. This article develops a similar automatic motor behavior for a robot and reports experiments with a one degree-of-freedom system. In a postural control task, the robot automatically adapts torque to counter a slow disturbance and shifts to increasing its stiffness when the disturbance increases in frequency. In the presence of rigid obstacles, it refrains from increasing force excessively, and relaxes gradually to follow the obstacle, but comes back to the desired state when the obstacle is removed. A trajectory tracking task demonstrates that the robot is able to adapt to different loads during motion. On introduction of a new load, it increases its stiffness to adapt to the load quickly, and then relaxes once the adaptation is complete. Furthermore, in the presence of an obstacle, the robot adjusts its trajectory to go around it. ©2010 IEEE.
Yeong CF, Baker K, Melendez-Calderon A, et al., 2010, ReachMAN to help sub-acute patients training reaching and manipulation, Pages: 90-95
This paper presents the control strategy and efficient tasks for training with ReachMAN, a compact, portable 3 degree-of-freedom robot to train reaching, pronosupination and grasping, independently or in combination. A pilot study was performed with three sub-acute patients to evaluate the potential use of ReachMAN as a rehabilitation tool, and determine how it should be used. All subjects improved their motor function, and gains in the range and quality of movements were seen, which are not detectable by typical functional assessment. ©2010 IEEE.
Burdet E, 2010, Disability and Rehabilitation: Assistive Technology: Editorial, Disability and Rehabilitation: Assistive Technology, Vol: 5, ISSN: 1748-3107
Dovat L, Lambercy O, Salman B, et al., 2010, A technique to train finger coordination and independence after stroke., Disabil Rehabil Assist Technol, Vol: 5, Pages: 279-287
PURPOSE: Finger coordination and independence are often impaired in stroke survivors, preventing them from performing activities of daily living. We have developed a technique using a robotic interface, the HandCARE, to train these functions. METHOD: The Hand Cable-Actuated REhabilitation (CARE) system can assist the subject in opening and closing movements of the hand, and can be adapted to accommodate various hand sizes and finger shapes. RESULTS: Two game-like exercises have been developed, which use a motivating approach to promote recovery of finger coordination and independence. Mathematical measures have been implemented to evaluate these functions. This technique is validated with two post-stroke subjects who practiced for 20 minutes twice a week during eight weeks. CONCLUSIONS: The results show significant improvements in finger coordination, force modulation as well as finger independence.
Ganesh G, Haruno M, Kawato M, et al., 2010, Motor Memory and Local Minimization of Error and Effort, Not Global Optimization, Determine Motor Behavior, JOURNAL OF NEUROPHYSIOLOGY, Vol: 104, Pages: 382-390, ISSN: 0022-3077
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- Citations: 67
Zhao G, Teo CL, Hutmacher DW, et al., 2010, Force-controlled automatic microassembly of tissue engineering scaffolds, JOURNAL OF MICROMECHANICS AND MICROENGINEERING, Vol: 20, ISSN: 0960-1317
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- Citations: 5
Arichi T, Moraux A, Melendez A, et al., 2010, Somatosensory cortical activation identified by functional MRI in preterm and term infants, NEUROIMAGE, Vol: 49, Pages: 2063-2071, ISSN: 1053-8119
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- Citations: 77
Tee KP, Franklin DW, Kawato M, et al., 2010, Concurrent adaptation of force and impedance in the redundant muscle system, BIOLOGICAL CYBERNETICS, Vol: 102, Pages: 31-44, ISSN: 0340-1200
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- Citations: 69
Zhao G, Teo CL, Hutmacher DW, et al., 2010, Automated Microassembly of Tissue Engineering Scaffold, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 1082-+, ISSN: 1050-4729
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