302 results found
Huang Y, Lai W, Cao L, et al., 2021, A Three-Limb Teleoperated Robotic System with Foot Control for Flexible Endoscopic Surgery., Ann Biomed Eng
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
Kuehn J, Bagnato C, Burdet E, et al., 2021, Arm movement adaptation to concurrent pain constraints, SCIENTIFIC REPORTS, Vol: 11, ISSN: 2045-2322
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 Clin, Vol: 30
Dystonia is a disorder of sensorimotor integration associated with abnormal oscillatory activity within the basal ganglia-thalamo-cortical networks. Event-related changes in spectral EEG activity reflect cortical processing but are sparsely investigated in relation to sensorimotor processing in dystonia. This study investigates modulation of sensorimotor cortex EEG activity in response to a proprioceptive stimulus in children with dystonia and dystonic cerebral palsy (CP). Proprioceptive stimuli, comprising brief stretches of the wrist flexors, were delivered via a robotic wrist interface to 30 young people with dystonia (20 isolated genetic/idiopathic and 10 dystonic CP) and 22 controls (mean age 12.7 years). Scalp EEG was recorded using the 10-20 international system and the relative change in post-stimulus power with respect to baseline was calculated for the alpha (8-12 Hz) and beta (14-30 Hz) frequency bands. A clear developmental profile in event-related spectral changes was seen in controls. Controls showed a prominent early alpha/mu band event-related desynchronisation (ERD) followed by an event-related synchronisation (ERS) over the contralateral sensorimotor cortex following movement of either hand. The alpha ERD was significantly smaller in the dystonia groups for both dominant and non-dominant hand movement (ANCOVA across the 3 groups with age as covariate: dominant hand F(2,47) = 4.45 p = 0.017; non-dominant hand F(2,42) = 9.397 p < 0.001. Alpha ERS was significantly smaller in dystonia for the dominant hand (ANCOVA F(2,47) = 7.786 p = 0.001). There was no significant difference in ERD or ERS between genetic/idiopathic dystonia and dystonic CP. CONCLUSION: Modulation of alpha/mu activity by a proprioceptive stimulus is reduced in dystonia, demonstrating a developmental abnormality of sensorimotor processing which is common to isolated genetic/idiopathic and acquired dystonia/d
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.
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
Huang Y, Eden J, Cao L, et al., 2020, Tri-Manipulation: An Evaluation of Human Performance in 3-Handed Teleoperation, IEEE Transactions on Medical Robotics and Bionics, Vol: 2, Pages: 545-548
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
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
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
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
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
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
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
Takagi A, Li Y, Burdet E, 2020, Flexible assimilation of human&#x0027;s target for versatile human-robot physical interaction, IEEE Transactions on Haptics, ISSN: 1939-1412
Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from assistance to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. We demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours.
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
Li R, Li Y, Li SE, et al., 2020, Indirect Shared Control for Cooperative Driving Between Driver and Automation in Steer-by-Wire Vehicles, IEEE Transactions on Intelligent Transportation Systems, Pages: 1-11, ISSN: 1524-9050
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
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
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.
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.
Perez NP, Tokarchuk L, Burdet E, et al., 2019, Exploring user motor behaviour in bimanual interactive video games, ISSN: 2325-4270
Video games have proved very valuable in rehabilitation technologies. They guide therapy and keep patients engaged and motivated. However, in order to realize their full potential, a good understanding is required of the players' motor control. In particular, little is known regarding player behaviour in tasks demanding bimanual interaction. In this work, an experiment was designed to improve the understanding of such tasks. A driving game was developed in which players were asked to guide a differential wheeled robot (depicted as a rocket) along a trajectory. The rocket could be manipulated by using an Xbox controller's triggers, each supplying torque to the corresponding side of the robot. Such a task is redundant, i.e. there exists an infinite number of input combinations to yield a given outcome. This allows the player to strategize according to their own preference. 10 participants were recruited to play this game and their input data was logged for subsequent analysis. Two different motor strategies were identified: an "intermittent" input pattern versus a "continuous" one. It is hypothesized that the choice of behaviour depends on motor skill and minimization of effort and error. Further testing is necessary to determine the exact relationship between these aspects.
Lotay R, Mace M, Rinne P, et al., 2019, optimizing self-exercise scheduling in motor stroke using Challenge Point Framework theory, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 435-440
An important challenge for technology-assisted self-led rehabilitation is how to automate appropriate schedules of exercise that are responsive to patients’ needs, and optimal for learning. While random scheduling has been found to be superior for long-term learning relative to fixed scheduling (Contextual Interference), this method is limited by not adequately accounting for task difficulty, or skill acquisition during training. One method that combines contextual interference with adaptation of the challenge to the skill-level of the player is Challenge Point Framework (CPF) theory. In this pilot study we test whether self-led motor training based upon CPF scheduling achieves faster learning than deterministic, fixed scheduling. Training was implemented in a mobile gaming device adapted for arm disability, allowing for grip and wrist exercises. We tested 11 healthy volunteers and 12 hemiplegic stroke patients in a single-blinded no crossover controlled randomized trial. Results suggest that patients training with CPF-based adaption performed better than those training with fixed conditions. This was not seen for healthy volunteers whose performance was close to ceiling. Further data collection is required to determine the significance of the results.
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