neural prosthesis

Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm...

Myoelectric devices are controlled by electromyographic signals generated by contraction of residual muscles, which thus serve as biological amplifiers of neural control signals. Although nerves severed by amputation continue to carry motor control information intended for the missing limb, loss of muscle effectors due to amputation prevents access to this important control information. Targeted Muscle Reinnervation (TMR) was developed as a novel strategy to improve control of myoelectric upper limb prostheses...

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback...

The walking mechanism of a prosthetic leg user is a tightly coordinated movement of several joints and limb segments. The interaction among the voluntary and mechanical joints and segments requires particular biomechanical insight. This study aims to analyze the inter-relationship between amputees' voluntary and mechanical coupled leg joints variables using cyclograms. From this analysis, the critical gait parameters in each gait phase were determined and analyzed if they contribute to a better powered prosthetic knee control design...

OBJECTIVE: Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. APPROACH: We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis...

Cognitive neural prosthesis is a manmade device which can be used to restore or compensate for lost human cognitive modalities. The generalized Laguerre-Volterra (GLV) network serves as a robust mathematical underpinning for the development of such prosthetic instrument. In this paper, a hardware implementation scheme of Gauss error function for the GLV network targeting reconfigurable platforms is reported. Numerical approximations are formulated which transform the computation of nonelementary function into combinational operations of elementary functions, and memory-intensive look-up table (LUT) based approaches can therefore be circumvented...

BACKGROUND: Regenerative Peripheral Nerve Interfaces (RPNIs) are neurotized muscle grafts intended to produce electromyographic signals suitable for motorized prosthesis control. Two RPNIs producing independent agonist/antagonist signals are required for each control axis; however, it is unknown whether signals from adjacent RPNIs are independent. The purpose of this work was to determine signaling characteristics from two adjacent RPNIs, the first neurotized by a foot dorsi-flexor nerve and the second neurotized by a foot plantar-flexor nerve in a rodent model...

BACKGROUND: The objective of surgical management of spinal metastases is to reduce pain and improve the patient's quality of life. The operation should restore spinal stability and decompress neural structures. One surgical technique is corpectomy followed by vertebral body reconstruction and stabilisation of the spine. The procedure may be performed in patients in overall good health and a good survival prognosis. The aim of this paper is to present the outcomes of surgical management of spinal metastases in patients who underwent corpectomy followed by vertebral body reconstruction and stabilisation of the spine...

This study aimed to observe the morphological characteristics of a PGLA [poly(glycolide-co-L-lactide)] nerve conduit and regenerated nerve bundle in the human body using high-frequency ultrasound and examine functional recovery of the regenerated nerve using functional magnetic resonance imaging (fMRI) after neural prosthesis with a PGLA nerve conduit. Thirty-nine patients underwent high-frequency ultrasound, and one patient with superficial radial nerve injury (27-mm defect) underwent fMRI at one, three, and six postoperative months...

Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming...

Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed approach is to estimate electrical stimuli that are equivalent to a given natural visual stimulus, in a way such that they both elicit responses that are as similar as possible when introduced to a Lateral Geniculate Nucleus (LGN) population...

Neural-machine interface (NMI) decoding errors challenge the clinical value of neural control of powered artificial legs, because these errors can dangerously disturb the user's walking balance, cause stumbles or falls, and thus threaten the user's confidence and safety in prosthesis use. Although extensive research efforts have been made to minimize the NMI decoding error rate, none of the current approaches can completely eliminate the errors in NMI. This study aimed at improving the robustness of prosthesis control system against neural decoding errors by introducing a fault-tolerant control (FTC) strategy...

Consistent, robust performance is critical for the utility and user-acceptance of neurally-controlled powered upper limb prostheses. We preliminarily evaluated the performance consistency of an electromyography (EMG)-driven controller based on a two degree-of-freedom musculoskeletal hand model, whose simplified structure is more practical for real-time prosthesis control than existing, complex models. Parameters of four virtual muscles were computed by numerical optimization from an able-bodied subject's kinematic and EMG data collected during wrist and metacarpophalangeal (MCP) flexion/extension movements...

In this paper, we demonstrate how automatic grasp selection can be achieved by placing a camera in the palm of a prosthetic hand and training a convolutional neural network on images of objects with corresponding grasp labels. Our labeled dataset is built from common graspable objects curated from the ImageNet dataset and from images captured from our own camera that is placed in the hand. We achieve a grasp classification accuracy of 93.2% and show through real-time grasp selection that using a camera to augment current electromyography controlled prosthetic hands may be useful...

Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming...

Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed approach is to estimate electrical stimuli that are equivalent to a given natural visual stimulus, in a way such that they both elicit responses that are as similar as possible when introduced to a Lateral Geniculate Nucleus (LGN) population...

Neural-machine interface (NMI) decoding errors challenge the clinical value of neural control of powered artificial legs, because these errors can dangerously disturb the user's walking balance, cause stumbles or falls, and thus threaten the user's confidence and safety in prosthesis use. Although extensive research efforts have been made to minimize the NMI decoding error rate, none of the current approaches can completely eliminate the errors in NMI. This study aimed at improving the robustness of prosthesis control system against neural decoding errors by introducing a fault-tolerant control (FTC) strategy...

In this paper, we demonstrate how automatic grasp selection can be achieved by placing a camera in the palm of a prosthetic hand and training a convolutional neural network on images of objects with corresponding grasp labels. Our labeled dataset is built from common graspable objects curated from the ImageNet dataset and from images captured from our own camera that is placed in the hand. We achieve a grasp classification accuracy of 93.2% and show through realtime grasp selection that using a camera to augment current electromyography controlled prosthetic hands may be useful...

Consistent, robust performance is critical for the utility and user-acceptance of neurally-controlled powered upper limb prostheses. We preliminarily evaluated the performance consistency of an electromyography (EMG)-driven controller based on a two degree-of-freedom musculoskeletal hand model, whose simplified structure is more practical for real-time prosthesis control than existing, complex models. Parameters of four virtual muscles were computed by numerical optimization from an able-bodied subject's kinematic and EMG data collected during wrist and metacarpophalangeal (MCP) flexion/extension movements...

OBJECTIVE: Proprioceptive mechanisms play a critical role in both reflexive and volitional lower extremity control. Significant strides have been made in the development of bionic limbs that are capable of bi-directional communication with the peripheral nervous system, but none of these systems have been capable of providing physiologically-relevant muscle-based proprioceptive feedback through natural neural pathways. In this study, we present the agonist-antagonist myoneural interface (AMI), a surgical approach with the capacity to provide graded kinesthetic feedback from a prosthesis through mechanical activation of native mechanoreceptors within residual agonist-antagonist muscle pairs...