Publications
384 results found
Zhu TL, Klein J, Dual SA, et al., 2014, reachMAN2: A compact rehabilitation robot to train <i>reach</i>ing and <i>man</i>ipulation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 2107-2113, ISSN: 2153-0858
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- Citations: 8
Gardner M, Woodward R, Vaidyanathan R, et al., 2014, An Unobtrusive Vision System to Reduce the Cognitive Burden of Hand Prosthesis Control, 13th International Conference on Control Automation Robotics & Vision (ICARCV), Publisher: IEEE, Pages: 1279-1284, ISSN: 2474-2953
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- Citations: 5
Quijano-Gonzalez Y, Melendez-Calderon A, Burdet E, et al., 2014, Upper Limb Functional Assessment of Children with Cerebral Palsy Using a Sorting Box, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 2330-2333, ISSN: 1557-170X
Vanderborght B, Albu-Schaeffer A, Bicchi A, et al., 2013, Variable impedance actuators: A review, ROBOTICS AND AUTONOMOUS SYSTEMS, Vol: 61, Pages: 1601-1614, ISSN: 0921-8890
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- Citations: 679
Esmaeili M, Guy S, Dailey WD, et al., 2013, Subject-Specific Wrist Model Calibration and Application to Ergonomic Design of Exoskeletons, IEEE SENSORS JOURNAL, Vol: 13, Pages: 3293-3301, ISSN: 1530-437X
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- Citations: 4
Arichi T, Gordon-Williams R, Allievi A, et al., 2013, Computer-controlled stimulation for functional magnetic resonance imaging studies of the neonatal olfactory system, ACTA PAEDIATRICA, Vol: 102, Pages: 868-875, ISSN: 0803-5253
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- Citations: 20
Allievi AG, Melendez-Calderon A, Arichi T, et al., 2013, An fMRI Compatible Wrist Robotic Interface to Study Brain Development in Neonates, ANNALS OF BIOMEDICAL ENGINEERING, Vol: 41, Pages: 1181-1192, ISSN: 0090-6964
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- Citations: 19
Ganesh G, Burdet E, 2013, Motor planning explains human behaviour in tasks with multiple solutions, ROBOTICS AND AUTONOMOUS SYSTEMS, Vol: 61, Pages: 362-368, ISSN: 0921-8890
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- Citations: 20
Campolo D, Widjaja F, Xu H, et al., 2013, Analysis of Accuracy in Pointing with Redundant Hand-held Tools: A Geometric Approach to the Uncontrolled Manifold Method, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
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- Citations: 12
Yang C, Li Z, Burdet E, 2013, Human Like Learning Algorithm for Simultaneous Force Control and Haptic Identification, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 710-715, ISSN: 2153-0858
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- Citations: 9
Esmaeili M, Dailey W, Burdet E, et al., 2013, Ergonomic design of a wrist exoskeleton and its effects on natural motor strategies during redundant tasks, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3370-3375, ISSN: 1050-4729
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- Citations: 1
Jarrasse N, Kuhne M, Roach N, et al., 2013, Analysis of grasping strategies and function in hemiparetic patients using an instrumented object, 13th IEEE International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, ISSN: 1945-7898
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- Citations: 1
Esmaeili M, Jarrasse N, Dailey W, et al., 2013, Hyperstaticity for Ergonomic Design of a Wrist Exoskeleton, 13th IEEE International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, ISSN: 1945-7898
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- Citations: 4
Zhou S-H, Tan Y, Oetomo D, et al., 2013, Point-to-point Learning in Human Motor Systems, American Control Conference (ACC), Publisher: IEEE, Pages: 5923-5928, ISSN: 0743-1619
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- Citations: 4
Takagi A, Bagnato C, Burdet E, 2013, Does Physical Interaction Escalate?, 5th International Conference on Social Robotics (ICSR), Publisher: SPRINGER-VERLAG BERLIN, Pages: 579-580, ISSN: 0302-9743
Roach N, Hussain A, Burdet E, 2012, <i>Learning to Design Rehabilitation Devices Through the H</i>-<i>CARD Course</i> Project-Based Learning of Rehabilitation Technology Design, IEEE PULSE, Vol: 3, Pages: 51-58, ISSN: 2154-2287
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- Citations: 6
Jarrasse N, Charalambous T, Burdet E, 2012, A Framework to Describe, Analyze and Generate Interactive Motor Behaviors, PLOS ONE, Vol: 7, ISSN: 1932-6203
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- Citations: 108
Balasubramanian S, Colombo R, Sterpi I, et al., 2012, Robotic assessment of upper limb motor function after stroke., Am J Phys Med Rehabil, Vol: 91, Pages: S255-S269
Traditional assessment of a stroke subject's motor ability, carried out by a therapist who observes and rates the subject's motor behavior using ordinal measurements scales, is subjective, time consuming and lacks sensitivity. Rehabilitation robots, which have been the subject of intense inquiry over the last decade, are equipped with sensors that are used to develop objective measures of motor behaviors in a semiautomated way during therapy. This article reviews the current contributions of robot-assisted motor assessment of the upper limb. It summarizes the various measures related to movement performance, the models of motor recovery in stroke subjects and the relationship of robotic measures to standard clinical measures. It analyses the possibilities offered by current robotic assessment techniques and the aspects to address to make robotic assessment a mainstream motor assessment method.
Kadiallah A, Franklin DW, Burdet E, 2012, Generalization in Adaptation to Stable and Unstable Dynamics, PLOS ONE, Vol: 7, ISSN: 1932-6203
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- Citations: 19
Masia L, Squeri V, Burdet E, et al., 2012, Wrist Coordination in a Kinematically Redundant Stabilization Task, IEEE TRANSACTIONS ON HAPTICS, Vol: 5, Pages: 231-239, ISSN: 1939-1412
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- Citations: 3
Balasubramanian S, Melendez-Calderon A, Burdet E, 2012, A Robust and Sensitive Metric for Quantifying Movement Smoothness, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 59, Pages: 2126-2136, ISSN: 0018-9294
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- Citations: 232
Arichi T, Fagiolo G, Varela M, et al., 2012, Development of BOLD signal hemodynamic responses in the human brain, Neuroimage, Vol: 63, Pages: 663-673, ISSN: 1095-9572
In the rodent brain the hemodynamic response to a brief external stimulus changes significantly during development. Analogous changes in human infants would complicate the determination and use of the hemodynamic response function (HRF) for functional magnetic resonance imaging (fMRI) in developing populations. We aimed to characterize HRF in human infants before and after the normal time of birth using rapid sampling of the Blood Oxygen Level Dependent (BOLD) signal. A somatosensory stimulus and an event related experimental design were used to collect data from 10 healthy adults, 15 sedated infants at term corrected post menstrual age (PMA) (median 41 + 1 weeks), and 10 preterm infants (median PMA 34 + 4 weeks). A positive amplitude HRF waveform was identified across all subject groups, with a systematic maturational trend in terms of decreasing time-to-peak and increasing positive peak amplitude associated with increasing age. Application of the age-appropriate HRF models to fMRI data significantly improved the precision of the fMRI analysis. These findings support the notion of a structured development in the brain's response to stimuli across the last trimester of gestation and beyond.
Zhou S-H, Oetomo D, Tan Y, et al., 2012, Modeling Individual Human Motor Behavior Through Model Reference Iterative Learning Control, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 59, Pages: 1892-1901, ISSN: 0018-9294
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- Citations: 16
Hussain A, Roach N, Balasubramanian S, et al., 2012, A modular sensor-based system for the Rehabilitation and Assessment of manipulation, Pages: 247-254
Task-oriented training can reduce motor impairments and improve functional outcomes in subjects affected by stroke. However in most cases this training cannot be performed with sufficient intensity due to limited human and financial resources. Therefore, we are developing a sensor-based System for Independent Task-oriented Assessment and Rehabilitation (SITAR), using an inertial wrist strap, a set of intelligent objects, a marker less camera-based movement tracking system, and dedicated rehabilitative computer games. This paper first presents the Multi-modal Interactive Motor Assessment And Training Environment (MIMATE), a hardware/software platform that serves as a generic basis for designing the intelligent objects and inertial wrist watch. It then describes the design and testing of three intelligent objects - a can, jar and key apparatus - developed to train and assess representative hand functions. Three different tests using these objects were performed on two healthy volunteers, showing how they enable an accurate characterization of the motor behavior during functional tasks. © 2012 IEEE.
Burdet E, Sanguineti V, Heuer H, et al., 2012, Motor Skill Learning and Neuro-Rehabilitation, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 20, Pages: 237-238, ISSN: 1534-4320
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- Citations: 5
Haruno M, Ganesh G, Burdet E, et al., 2012, Differential neural correlates of reciprocal activation and cocontraction control in dorsal and ventral premotor cortices, JOURNAL OF NEUROPHYSIOLOGY, Vol: 107, Pages: 126-133, ISSN: 0022-3077
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- Citations: 10
Campolo D, Widjaja F, Xu H, et al., 2012, A geometric approach to the Uncontrolled Manifold analysis, 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) / Symposium on Surgical Robotics, Publisher: IEEE, Pages: 1223-1228, ISSN: 2155-1782
Ganesh G, Jarrasse N, Haddadin S, et al., 2012, A versatile biomimetic controller for contact tooling and haptic exploration, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3329-3334, ISSN: 1050-4729
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- Citations: 35
Vanderborght B, Albu-Schaeffer A, Bicchi A, et al., 2012, Variable Impedance Actuators: Moving the Robots of Tomorrow, 25th IEEE\RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 5454-+, ISSN: 2153-0858
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- Citations: 32
Zhou SH, Oetomo D, Tan Y, et al., 2011, Optimal learning gain selection in model reference iterative learning control algorithms for human motor systems, Pages: 338-344
The role of learning gains in the ability of a computational framework to better capture the behaviour of human motor control in learning and executing a task is the subject of discussion in this paper. In our previous work, a computational model for human motor learning of a task through repetition was established and its convergence analysed. In this paper, the performance of the model is investigated through the addition of degrees of freedom in selecting learning gains, specifically the ability to independently select the learning gain for the damping term. A particle swarm optimisation (PSO) algorithm is utilised to obtain a set of gains optimised to reduce the discrepancy between the experimental data and the simulated trajectories. It is found that it is possible to improve the accuracy of the computational model through the appropriate choice of learning gains. The results and interesting findings are presented and discussed in this paper. © 2011 ENGINEERS AUSTRALIA & AUSTRALIAN OPTICAL.
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