Publications
384 results found
Noccaro A, Eden J, Di Pino G, et al., 2021, Human performance in three-hands tasks., Scientific Reports, Vol: 11, Pages: 1-8, ISSN: 2045-2322
The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of more than two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination with a partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: (1) how does simultaneous control of three hands compare to a cooperating dyad; (2) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of task score, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.
Huang Y, Lai W, Cao L, et al., 2021, A three-limb teleoperated robotic system with foot control for flexible endoscopic surgery, Annals of Biomedical Engineering, Vol: 49, Pages: 2282-2296, ISSN: 0090-6964
Flexible endoscopy requires a lot of skill to manipulate both the endoscope and the associated instruments. In most robotic flexible endoscopic systems, the endoscope and instruments are controlled separately by two operators, which may result in communication errors and inefficient operation. Our solution is to enable the surgeon to control both the endoscope and the instruments. Here, we present a novel tele-operation robotic endoscopic system commanded by one operator using the continuous and simultaneous movements of their two hands and one foot. This 13-degree-of-freedom (DoF) system integrates a foot-controlled robotic flexible endoscope and two hand-controlled robotic endoscopic instruments, a robotic grasper and a robotic cauterizing hook. A dedicated foot-interface transfers the natural foot movements to the 4-DoF movements of the endoscope while two other commercial hand interfaces map the movements of the two hands to the two instruments individually. An ex-vivo experiment was carried out by six subjects without surgical experience, where the simultaneous control with foot and hands was compared with a sequential clutch-based hand control. The participants could successfully teleoperate the endoscope and the two instruments to cut the tissues at scattered target areas in a porcine stomach. Foot control yielded 43.7% faster task completion and required less mental effort as compared to the clutch-based hand control scheme, which proves the concept of three-limb tele-operation surgery and the developed flexible endoscopic system.
Huang Y, Lai W, Cao L, et al., 2021, Design and Evaluation of a Foot-Controlled Robotic System for Endoscopic Surgery, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 6, Pages: 2469-2476, ISSN: 2377-3766
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- Citations: 8
Kuehn J, Bagnato C, Burdet E, et al., 2021, Arm movement adaptation to concurrent pain constraints, Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322
How do humans coordinate their movements in order to avoid pain? This paper investigates a motor task in the presence of concurrent potential pain sources: the arm must be withdrawn to avoid a slap on the hand while avoiding an elbow obstacle with an electrical noxious stimulation. The results show that our subjects learned to control the hand retraction movement in order to avoid the potential pain. Subject-specific motor strategies were used to modify the joint movement coordination to avoid hitting the obstacle with the elbow at the cost of increasing the risk of hand slap. Furthermore, they used a conservative strategy as if assuming an obstacle in 100% of the trials.
Dall'Orso S, Fifer WP, Balsam PD, et al., 2021, Cortical processing of multimodal sensory learning in human neonates, Cerebral Cortex, Vol: 31, Pages: 1827-1836, ISSN: 1047-3211
Following birth, infants must immediately process and rapidly adapt to the array of unknown sensory experiences associated with their new ex-utero environment. However, although it is known that unimodal stimuli induce activity in the corresponding primary sensory cortices of the newborn brain, it is unclear how multimodal stimuli are processed and integrated across modalities. The latter is essential for learning and understanding environmental contingencies through encoding relationships between sensory experiences; and ultimately likely subserves development of life-long skills such as speech and language. Here, for the first time, we map the intracerebral processing which underlies auditory-sensorimotor classical conditioning in a group of 13 neonates (median gestational age at birth: 38 weeks + 4 days, range: 32 weeks + 2 days to 41 weeks + 6 days; median postmenstrual age at scan: 40 weeks + 5 days, range: 38 weeks + 3 days to 42 weeks + 1 days) with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (MRI) and magnetic resonance (MR) compatible robotics. We demonstrate that classical conditioning can induce crossmodal changes within putative unimodal sensory cortex even in the absence of its archetypal substrate. Our results also suggest that multimodal learning is associated with network wide activity within the conditioned neural system. These findings suggest that in early life, external multimodal sensory stimulation and integration shapes activity in the developing cortex and may influence its associated functional network architecture.
Huang HY, Farkhatdinov I, Arami A, et al., 2021, Cable-driven robotic interface for lower limb neuromechanics identification, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 461-469, ISSN: 0018-9294
This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle in the sagittal plane. This endpoint-based interface offers highly dynamic interaction and accurate position control (as is typically required for neuromechanics identification), and provides measurements of position, interaction force and EMG of leg muscles. It can be used with the subject upright, corresponding to a natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500 N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.
McClelland VM, Fischer P, Foddai E, et al., 2021, EEG measures of sensorimotor processing and their development are abnormal in children with isolated dystonia and dystonic cerebral palsy, NEUROIMAGE-CLINICAL, Vol: 30, ISSN: 2213-1582
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- Citations: 5
Sena A, Rouxel Q, Ivanova E, et al., 2021, Haptic Bimanual System for Teleoperation of Time-Delayed Tasks, IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO), Publisher: IEEE, Pages: 1234-1239
Blondin CM, Ivanova E, Eden J, et al., 2021, Perception and Performance of Electrical Stimulation for Proprioception, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 4550-4554, ISSN: 1557-170X
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- Citations: 1
Huang Y, Eden J, Ivanova E, et al., 2021, Trimanipulation: Evaluation of human performance in a 3-handed coordination task, IEEE International Conference on Systems, Man, and Cybernetics (SMC), Publisher: IEEE, Pages: 882-887, ISSN: 1062-922X
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- Citations: 4
Sakellariou DF, Dall'Orso S, Burdet E, et al., 2020, Abnormal microscale neuronal connectivity triggered by a proprioceptive stimulus in dystonia, Scientific Reports, Vol: 10, Pages: 1-12, ISSN: 2045-2322
We investigated modulation of functional neuronal connectivity by a proprioceptive stimulus in sixteen young people with dystonia and eight controls. A robotic wrist interface delivered controlled passive wrist extension movements, the onset of which was synchronised with scalp EEG recordings. Data were segmented into epochs around the stimulus and up to 160 epochs per subject were averaged to produce a Stretch Evoked Potential (StretchEP). Event-related network dynamics were estimated using a methodology that features Wavelet Transform Coherency (WTC). Global Microscale Nodal Strength (GMNS) was introduced to estimate overall engagement of areas into short-lived networks related to the StretchEP, and Global Connectedness (GC) estimated the spatial extent of the StretchEP networks. Dynamic Connectivity Maps showed a striking difference between dystonia and controls, with particularly strong theta band event-related connectivity in dystonia. GC also showed a trend towards higher values in dystonia than controls. In summary, we demonstrate the feasibility of this method to investigate event-related neuronal connectivity in relation to a proprioceptive stimulus in a paediatric patient population. Young people with dystonia show an exaggerated network response to a proprioceptive stimulus, displaying both excessive theta-band synchronisation across the sensorimotor network and widespread engagement of cortical regions in the activated network.
Takagi A, De Magistris G, Xiong G, et al., 2020, Analogous adaptations in speed, impulse and endpoint stiffness when learning a real and virtual insertion task with haptic feedback, Scientific Reports, Vol: 10, ISSN: 2045-2322
Humans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.
Lo Presti D, Dall'Orso S, Muceli S, et al., 2020, An fMRI compatible smart device for measuring palmar grasping actions in newborns, Sensors, Vol: 20, Pages: 1-16, ISSN: 1424-8220
Grasping is one of the first dominant motor behaviors that enable interaction of a newborn infant with its surroundings. Although atypical grasping patterns are considered predictive of neuromotor disorders and injuries, their clinical assessment suffers from examiner subjectivity, and the neuropathophysiology is poorly understood. Therefore, the combination of technology with functional magnetic resonance imaging (fMRI) may help to precisely map the brain activity associated with grasping and thus provide important insights into how functional outcomes can be improved following cerebral injury. This work introduces an MR-compatible device (i.e., smart graspable device (SGD)) for detecting grasping actions in newborn infants. Electromagnetic interference immunity (EMI) is achieved using a fiber Bragg grating sensor. Its biocompatibility and absence of electrical signals propagating through the fiber make the safety profile of the SGD particularly favorable for use with fragile infants. Firstly, the SGD design, fabrication, and metrological characterization are described, followed by preliminary assessments on a preterm newborn infant and an adult during an fMRI experiment. The results demonstrate that the combination of the SGD and fMRI can safely and precisely identify the brain activity associated with grasping behavior, which may enable early diagnosis of motor impairment and help guide tailored rehabilitation programs.
Huang Y, Eden J, Cao L, et al., 2020, Trimanipulation: an evaluation of human performance in 3-handed teleoperation, IEEE Transactions on Medical Robotics and Bionics, Vol: 2, Pages: 545-548, ISSN: 2576-3202
With a third hand a human subject could be able to perform tasks that they would otherwise be incapable of. Foot-controlled interfaces have proven suitable for controlling robot arms, however, when it is best to use such devices and what limitations they place when controlling a supernumerary robotic limb (SL) in combination with the natural limbs (NLs) is unknown. Here, we report an investigation of three-handed manipulation with a foot interface. We analysed i) what effect the addition of a SL has on the performance of the NLs; and ii) how mechanical and/or cognitive coupling alters user performance. When the subjects moved the three limbs without coupling, only the motion’s completion time was observed to be significantly affected. In contrast with coupling, the incorporation of the foot controlled hand was found to affect the success rate, and planning of the motion, however it did not affect motion characteristics such as smoothness.
Broderick M, Bentley P, Burridge J, et al., 2020, SELF-ADMINISTERED GAMING EXERCISES FOR STROKE ARM DISABILITY INCREASE EXERCISE DURATION BY MORE THAN TWO-FOLD AND REPETITIONS MORE THAN TEN-FOLD COMPARED TO STANDARD CARE, Publisher: SAGE PUBLICATIONS LTD, Pages: 255-255, ISSN: 1747-4930
Gardner M, Mancero Castillo C, Wilson S, et al., 2020, A multimodal intention detection sensor suite for shared autonomy of upper-limb robotic prostheses, Sensors, Vol: 20, ISSN: 1424-8220
Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.
Atashzar SF, Huang H-Y, Duca FD, et al., 2020, Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 5953-5960, ISSN: 2377-3766
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- Citations: 6
Arami A, van Asseldonk E, van der Kooij H, et al., 2020, A Clustering-Based Approach to Identify Joint Impedance During Walking, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 28, Pages: 1808-1816, ISSN: 1534-4320
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- Citations: 8
Li Y, Eden J, Carboni G, et al., 2020, Improving tracking through human-robot sensory augmentation, IEEE Robotics and Automation Letters, Vol: 5, Pages: 4399-4406, ISSN: 2377-3766
This letter introduces a sensory augmentation technique enabling a contact robot to understand its human user's control in real-time and integrate their reference trajectory information into its own sensory feedback to improve the tracking performance. The human's control is formulated as a feedback controller with unknown control gains and desired trajectory. An unscented Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate a reference trajectory. Simulations and an implementation on a robotic interface demonstrate that the reactive control can robustly identify the human user's control, and that the sensory augmentation improves the robot's tracking performance.
Varghese RJ, Nguyen A, Burdet E, et al., 2020, Nonlinearity compensation in a multi-DoF shoulder sensing exosuit for real-time teleoperation, 3rd IEEE International Conference on Soft Robotics (RoboSoft), Publisher: IEEE, Pages: 668-675
The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi- DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.
Takagi A, Maxwell S, Melendez-Calderon A, et al., 2020, The dominant limb preferentially stabilizes posture in a bimanual task with physical coupling, Journal of Neurophysiology, Vol: 123, Pages: 2154-2160, ISSN: 0022-3077
Humans are endowed with an ability to skillfully handle objects, like when holding a jar with the non-dominant hand whilst opening the lid with the dominant hand. Dynamic-dominance, a prevailing theory in handedness research, proposed that the non-dominant hand is specialized for postural stability, which would explain why right-handers hold the jar steady using the left hand. However, the underlying specialization of the non-dominant hand has only been tested unimanually, or in a bimanual task where the two hands had different functions. Using a dedicated dual robotic wrist interface, we could test the dynamic-dominance hypothesis in a bimanual task where both hands carry out the same function. We examined how left- and right-handed subjects held onto a vibrating virtual object using their wrists, which were physically coupled through the object. Muscular activity of the wrist flexors and extensors revealed a preference for cocontracting the dominant hand. Such stabilization action contradicts the dynamic-dominance hypothesis. While the reliance on the dominant hand was partially explained by its greater strength, the Edinburgh inventory was a better predictor of the difference in the cocontraction between the dominant and non-dominant hands. When provided with redundancy to stabilize the task, the dominant hand preferentially cocontracts to absorb perturbing forces.
Gia-Hoang P, Hansen C, Tommasino P, et al., 2020, Estimating Human Wrist Stiffness during a Tooling Task, SENSORS, Vol: 20
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- Citations: 6
Huang HY, Arami A, Farkhatdinov I, et al., 2020, The Influence of Posture, Applied Force and Perturbation Direction on Hip Joint Viscoelasticity, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 28, Pages: 1138-1145, ISSN: 1534-4320
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- Citations: 9
Ivanova E, Carboni G, Eden J, et al., 2020, For motion assistance humans prefer to rely on a robot rather than on an unpredictable human, IEEE Open Journal of Engineering in Medicine and Biology, Vol: 1, Pages: 133-139, ISSN: 2644-1276
Objective: The last decades have seen a surge of robots for physical training and work assistance. How to best control these interfaces is unknown, although arguably the interaction should be similar to human movement assistance. Methods: We compare the behaviour and assessment of subjects tracking a moving target with assistance from (i) trajectory guidance (as typically used in robots for physical training), (ii) a human partner, and (iii) the reactive robot partner of Takagi et al. Results: Trajectory guidance was recognised as robotic, while the robot partner was felt as human-like. However, trajectory guidance was preferred to assistance from a human partner, which was recognised as less predictable. The robot partner also was felt to be more predictable and helpful than a human partner, and was preferred. Conclusions: While subjects like to rely on predictable interaction, such as in trajectory guidance, the control reactivity of the robot partner is essential for perceiving an interaction as human-like.
Huang Y, Burdet E, Cao L, et al., 2020, A Subject-Specific Four-Degree-of-Freedom Foot Interface to Control a Surgical Robot, IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol: 25, Pages: 951-963, ISSN: 1083-4435
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- Citations: 47
Borzelli D, Burdet E, Pastorelli S, et al., 2020, Identification of the best strategy to command variable stiffness using electromyographic signals, JOURNAL OF NEURAL ENGINEERING, Vol: 17, ISSN: 1741-2560
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- Citations: 5
Luo J, Yang C, Burdet E, et al., 2020, Adaptive impedance control with trajectory adaptation for minimizing interaction force, 29th IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN), Publisher: IEEE, Pages: 1360-1365, ISSN: 1944-9445
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- Citations: 3
Huang Y, Burdet E, Cao L, et al., 2019, Performance evaluation of a foot interface to operate a robot arm, IEEE Robotics and Automation Letters, Vol: 4, Pages: 3302-3309, ISSN: 2377-3766
We developed a foot interface enabling an operator to control a robotic arm with four degrees of freedom in continuous direction and speed, for operating one of the multiple tools required during robot-aided surgery. In this letter, we first test whether this pedal interface can be used to carry out complex manipulation as is required in surgery. Second, we compare the performance of ten naive operators using this new interface and a traditional button interface providing axis-by-axis constant-speed control. Testing is carried out on geometrically complex path-following tasks similar to laparoscopic training. Movement precision, time and smoothness are analyzed. The results demonstrate that the continuous pedal interface can be used to control a robot in complex motion tasks. The subjects kept the average error rate at a low level of around 2.6% with both interfaces, but the pedal interface resulted in about 30% faster operation and 60% smoother movement, which indicates improved efficiency and user experience as compared with the button interface. A questionnaire shows that controlling the robot with the pedal interface was more intuitive, comfortable, and less tiring than with the button interface.
Dahiya R, Yogeswaran N, Liu F, et al., 2019, Large-Area Soft e-Skin: The Challenges Beyond Sensor Designs, PROCEEDINGS OF THE IEEE, Vol: 107, Pages: 2016-2033, ISSN: 0018-9219
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- Citations: 181
Arami A, Poulakakis-Daktylidis A, Tai YF, et al., 2019, Prediction of gait freezing in Parkinsonian patients: a binary classification augmented with time series prediction, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 27, Pages: 1909-1919, ISSN: 1534-4320
This paper presents a novel technique to predict freezing of gait in advance-stage Parkinsonian patients using movement data from wearable sensors. A two-class approach is presented which consists of autoregressive predictive models to project the feature time series, followed by machine learning based classifiers to discriminate freezing from nonfreezing based on the predicted features. To implement and validate our technique a set of time domain and frequency domain features were extracted from the 3D acceleration data, which was then analyzed using information theoretic and feature selection approaches to determine the most discriminative features. Predictive models were trained to predict the features from their past values, then fed into binary classifiers based on support vector machines and probabilistic neural networks which were rigorously cross validated. We compared the results of this approach with a three-class classification approach proposed in previous literature, in which a pre-freezing class was introduced and the problem of prediction of the gait freezing incident was reduced to solving a three-class classification problem. The twoclass approach resulted in a sensitivity of 93±4%, specificity of 91±6%, with an expected prediction horizon of 1.72 seconds. Our subject-specific gait freezing prediction algorithm outperformed existing algorithms, yields consistent results across different subjects and is robust against the choice of classifier, with slight variations in the selected features. In addition, we analyzed the merits and limitations of different families of features to predict gait freezing.
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