An implantable neuroprosthesis for standing and walking in paraplegia: 5-year patient follow-up

Guiraud, David; Stieglitz, Thomas; Koch, Klaus Peter; Divoux, Jean Louis; Rabischong, P.

doi:10.1088/1741-2560/3/4/003

Cited by 99 publications

(62 citation statements)

References 17 publications

(24 reference statements)

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“…Although most devices were originally developed for functional electrical stimulation (FES) in spinal cord injured persons [21][22][23], they can also be the key component of neurocontrolled hand prostheses. In this case, they are used to record efferent motor signals and to stimulate afferent nerves (i.e., in a complementary way with respect to FES systems) [4].…”

Section: Introductionmentioning

confidence: 99%

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

et al. 2010

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Abstract-The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this achievement. Among the various approaches, peripheral nerve based intra fascicular electrodes are excellent neural interface candidates, representing an excellent compromise between high selectivity and relatively low invasiveness. Moreover, this approach has undergone preliminary testing in human volunteers and has shown promises.In this manuscript, we investigated whether the use of intrafascicular electrodes can be used to decode multiple sensory and motor information channels with the aim to develop a finite state algorithm that may be employed to control neuroprostheses and neuro-controlled hand prostheses. The results achieved both in animal and human experiments show that the combination of multiple sites recordings and advanced signal processing techniques (such as wavelet denoising and spike sorting algorithms) can be used to identify both sensory stimuli (in animal models) and motor commands (in a human volunteer). These findings have interesting implications, which should be investigated in future experiments.

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Section: Introductionmentioning

confidence: 99%

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

et al. 2010

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“…It has been employed as a general method in modern rehabilitation to partially restore motor function for patients with upper neural lesions [1,2]. Recently, the rapid progress in microprocessor technology provided the means for computer-controlled FES systems [3][4][5]. A fundamental problem concerning FES is how to handle the high complexity and nonlinearity of the neuromusculoskeletal system [6,7].…”

Section: Muscle Modeling and Fesmentioning

confidence: 99%

In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

Hayashibe

Guiraud

Poignet

2013

ISRN Rehabilitation

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Skeletal muscle system has nonlinear dynamics and subject-specific characteristics. Thus, it is essential to identify the unknown parameters from noisy biomedical signals to improve the modeling accuracy in neuroprosthetic control. The objective of this work is to develop an experimental identification method for subject-specific biomechanical parameters of a physiological muscle model which can be employed to predict the nonlinear force properties of stimulated muscle. Our previously proposed muscle model, which can describe multiscale physiological system based on the Hill and Huxley models, was used for the identification. The identification protocols were performed on two rabbit experiments, where the medial gastrocnemius was attached to a motorized lever system to record the force by the nerve stimulation. The muscle model was identified using nonlinear Kalman filters: sigmapoint and extended Kalman filter. The identified model was evaluated by comparison with experimental measurements in the cross-validation manner. The feasibility could be demonstrated by comparison between the estimated parameter and the measured value. The estimates with SPKF showed 5.7% and 2.9% error in each experiment with 7 different initial conditions. It reveals that SPKF has great advantage especially for the identification of multiscale muscle model which accounts for the high nonlinearity and discontinuous states between muscle contraction and relaxation process.

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“…It also receives the sound picked up from a microphone implanted inside the cochlear. The sound is processed before being send to the logic control, which determines the shape, the amplitude and the duration of the stimulation current [7], [8].…”

Section: A Case Study: the Cochlear Implantmentioning

confidence: 99%

New dependability approach for implanted medical devices

Soulier

Floch

Bernard

et al. 2009

2009 International Conference on Microelectronics - ICM

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Abstract-Functional Electrical Stimulation (FES) is an attractive solution to restore some lost or failing physiological functions. Obviously, the FES system may be hazardous for patient and the reliability and dependability of the system must be maximal. Unfortunately, the present context, where the associated systems are more and more complex and their development needs very cross-disciplinary experts, is not favorable to safety. Moreover, the direct adaptation of the existing dependability techniques from domains such as space or automotive is not suitable.Firstly, this paper proposes a strategy for risk management at system level for FES medical implant. The idea is to give a uniform framework where all possible hazards are highlighted and associated consequences are minimized. Then, the paper focuses on one of the most critical part of the FES system: analog micro-circuit which generates the electrical signal to electrode. As this micro-circuit is the closest to the human tissue, any failure might involve very critical consequences for the patient. We propose a concurrent top-down and bottom-up approach where the critical elements are highlighted and an extended risk analysis is performed.

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An implantable neuroprosthesis for standing and walking in paraplegia: 5-year patient follow-up

Cited by 99 publications

References 17 publications

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

New dependability approach for implanted medical devices

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