Archives: Publications

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  Lew, Eileen; Chavarriaga, Ricardo; Silvoni, Stefano; Millán, José del R.

Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user’s intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation.

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  Dominici, N.; Keller, U.; Vallery, H.; Friedli, L.; Van Den Brand, R.; Starkey, M. L.; Musienko, P.; Riener, R.; Courtine, G.

Central nervous system (CNS) disorders distinctly impair locomotor pattern generation and balance, but technical limitations prevent independent assessment and rehabilitation of these subfunctions. Here we introduce a versatile robotic interface to evaluate, enable and train pattern generation and balance independently during natural walking behaviors in rats. In evaluation mode, the robotic interface affords detailed assessments of pattern generation and dynamic equilibrium after spinal cord injury (SCI) and stroke. In enabling mode, the robot acts as a propulsive or postural neuroprosthesis that instantly promotes unexpected locomotor capacities including overground walking after complete SCI, stair climbing following partial SCI and precise paw placement shortly after stroke. In training mode, robot-enabled rehabilitation, epidural electrical stimulation and monoamine agonists reestablish weight-supported locomotion, coordinated steering and balance in rats with a paralyzing SCI. This new robotic technology and associated concepts have broad implications for both assessing and restoring motor functions after CNS disorders, both in animals and in humans. © 2012 Nature America, Inc. All rights reserved.

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  Gallo, S.; Santos-Carreras, L.; Rognini, G.; Hara, M.; Yamamoto, A.; Higuchi, T.; Bleuler, H.

Surgical robotics is among the most challenging applications of motion control. Present and future systems are essentially master-slave systems. Our work focuses on force-feedback and haptic interfaces. In this context, we study multimodal haptic interfaces, i.e. The fusion of force-feedback, with other tactile information such as temperature or pressure. First results support the proposition that such multimodal haptic devices can help improve surgeon’s dexterity and motion control. In order to strengthen this point, we investigate the psychophysics of thermal perception. This paper presents a device for temperature feedback that can be integrated in a multimodal haptic console. A finger sized tactile temperature display able to generate temperature gradients under the fingertip is presented along with first measurement results. © 2012 IEEE.

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  Nagi, J.; Ducatelle, F.; Di Caro, G. A.; Cireşan, D.; Meier, U.; Giusti, A.; Nagi, F.; Schmidhuber, J.; Gambardella, L. M.

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance. © 2011 IEEE.