Journal articles: 'Neural prosthesis'

1

Lin, Xiangli. "Neurophysiology Based on Deep Neural Network under Artificial Prosthesis Vision." Journal of Physics: Conference Series 2074, no. 1 (November 1, 2021): 012083. http://dx.doi.org/10.1088/1742-6596/2074/1/012083.

Full text

Abstract:

Abstract With the vigorous development of electronic technology and computer technology, as well as the continuous advancement of research in the fields of neurophysiology, bionics and medicine, the artificial visual prosthesis has brought hope to the blind to restore their vision. Artificial optical prosthesis research has confirmed that prosthetic vision can restore part of the visual function of patients with non-congenital blindness, but the mechanism of early prosthetic image processing still needs to be clarified through neurophysiological research. The purpose of this article is to study neurophysiology based on deep neural networks under simulated prosthetic vision. This article uses neurophysiological experiments and mathematical statistical methods to study the vision of simulated prostheses, and test and improve the image processing strategies used to simulate the visual design of prostheses. In this paper, based on the low-pixel image recognition of the simulating irregular phantom view point array, the deep neural network is used in the image processing strategy of prosthetic vision, and the effect of the image processing method on object image recognition is evaluated by the recognition rate. The experimental results show that the recognition rate of the two low-pixel segmentation and low-pixel background reduction methods proposed by the deep neural network under simulated prosthetic vision is about 70%, which can significantly increase the impact of object recognition, thereby improving the overall recognition ability of visual guidance.

APA, Harvard, Vancouver, ISO, and other styles

2

Di, Giovanna, W. Gong, C. Haburcakova, V. Kögler, J. Carpaneto, V. Genovese, D. Merfeld, et al. "Development of a closed-loop neural prosthesis for vestibular disorders." Journal of Automatic Control 20, no. 1 (2010): 27–32. http://dx.doi.org/10.2298/jac1001027d.

Full text

Abstract:

Vestibular disorders can cause severe problems including spatial disorientation, imbalance, nausea, visual blurring, and even cognitive deficits. The CLONS project is developing a closed-loop, sensory neural prosthesis to alleviate these symptoms [1]. In this article, we outline the different components necessary to develop this prosthetic. A short version of this work was presented in the NEUREL 2010 [1]. Conceptually, the prosthesis restores vestibular information based on inertial sensors rigidly affixed to the user. These sensors provide information about rotational velocity of the head; the prosthetic then transfers the information to the vestibular nerve via electrical stimulation. Here we present a project overview, development details, and summarize our progress in animal models and selected human volunteers.

APA, Harvard, Vancouver, ISO, and other styles

3

Boshlyakov, Andrew A., and Alexander S. Ermakov. "Development of a Vision System for an Intelligent Robotic Hand Prosthesis Using Neural Network Technology." ITM Web of Conferences 35 (2020): 04006. http://dx.doi.org/10.1051/itmconf/20203504006.

Full text

Abstract:

A brief review of the existing auxiliary prosthetic control systems was carried out. The concept of an intelligent prosthesis is proposed, which will expand the possibilities of application and simplify the use of the prosthesis. The required actions of the vision system in automatic and manual capture modes are considered. The sequence of operation of the subsystems of the technical vision system is determined. The possibility of implementing a prosthesis vision system based on neural network technology is shown. The method of using a ready-made neural network for recognition of objects by a prosthesis is considered. The possibilities of using the considered neural network technologies in the mathematical education of engineers are presented. A version of the prosthesis design is proposed. The possibility of constructing the described prosthesis is shown.

APA, Harvard, Vancouver, ISO, and other styles

4

Pitkin, Mark, Charles Cassidy, Maxim A. Shevtsov, Joshua R. Jarrell, Hangue Park, Brad J. Farrell, John F. Dalton, et al. "Recent Progress in Animal Studies of the Skin- and Bone-integrated Pylon With Deep Porosity for Bone-Anchored Limb Prosthetics With and Without Neural Interface." Military Medicine 186, Supplement_1 (January 1, 2021): 688–95. http://dx.doi.org/10.1093/milmed/usaa445.

Full text

Abstract:

ABSTRACT Introduction The three major unresolved problems in bone-anchored limb prosthetics are stable, infection-free integration of skin with a percutaneous bone implant, robust skeletal fixation between the implant and host bone, and a secure interface of sensory nerves and muscles with a prosthesis for the intuitive bidirectional prosthetic control. Here we review results of our completed work and report on recent progress. Materials and Methods Eight female adult cats received skin- and bone-integrated pylon (SBIP) and eight male adult cats received SBIP-peripheral neural interface (PNI) pylon into the right distal tibia. The latter pylons provided PNI for connection between a powered sensing transtibial prosthesis and electrodes in residual soleus muscle and on residual distal tibial nerve. If signs of infection were absent 28-70 days after implantation, cats started wearing a passive prosthesis. We recorded and analyzed full-body mechanics of level and slope locomotion in five cats with passive prostheses and in one cat with a powered sensing prosthesis. We also performed histological analyses of tissue integration with the implants in nine cats. Four pigs received SBIPs into the left hindlimb and two pigs—into the left forelimb. We recorded vertical ground reaction forces before amputation and following osseointegration. We also conducted pullout postmortem tests on the implanted pylons. One pig received in dorsum the modified SBIPs with and without silver coating. Results Six cats from the SBIP groups had implant for 70 days. One cat developed infection and did not receive prosthesis. Five cats had pylon for 148 to 183 days, showed substantial loading of the prosthesis during locomotion (40.4% below presurgery control), and demonstrated deep ingrowth of skin and bone tissue into SBIP (over 60%). Seven of eight cats from the SBIP-PNI group demonstrated poor pylon integration without clinical signs of infection. One cat had prosthesis for 824 days (27 months). The use of the bidirectionally controlled prosthesis by this animal during level walking demonstrated increased vertical loading to nearly normal values, although the propulsion force was significantly reduced. From the study on pigs, it was found that symmetry in loading between the intact and prosthetic limbs during locomotion was 80 ± 5.5%. Skin-implant interface was infection-free, but developed a stoma, probably because of the high mobility of the skin and soft tissues in the pig’s thigh. Dorsal implantation resulted in the infection-free deep ingrowth of skin into the SBIP implants. Conclusions Cats with SBIP (n = 5) and SBIP-PNI (n = 1) pylons developed a sound interface with the residuum skin and bone and demonstrated substantial loading of prosthetic limb during locomotion. One animal with SBIP developed infection and seven cats with SBIP-PNI demonstrated poor bone integration without signs of infection. Future studies of the SBIP-PNI should focus on reliability of integration with the residuum. Ongoing study with pigs requires decreasing the extra mobility of skin and soft tissues until the skin seal is developed within the SBIP implant.

APA, Harvard, Vancouver, ISO, and other styles

5

Mundkur, Nipun. "Bionic Human: A Review of Interface Modalities for Externally Powered Prosthetic Limbs." McGill Science Undergraduate Research Journal 14, no. 1 (April 10, 2019): 46–49. http://dx.doi.org/10.26443/msurj.v14i1.53.

Full text

Abstract:

Background: The loss of a limb is a debilitating incident and can leave patients significantly disabled and often unable to perform activities of daily living. Prosthetic limbs can provide some modicum of normalcy back to their lives, and there has been much research over the past few decades into restoration of biomedical and physiological function with the use of externally powered and robotic prostheses. This review aims to explore the various approaches to machine-body interfacing that can be employed to achieve intuitive and meaningful control of these complex devices, and to discuss the individual benefits and drawbacks of each method. Methods: Studies looked at include both primary and secondary sources of research. Identification was via a PubMed search for the terms “prosthetic limb”, “powered prostheses”, “myoelectric prostheses”, “neural interface”, “prosthetic somatosensory feedback”, and “brain-machine interface”, which resulted in a total of 3892 papers retrieved. Of these, 28 were retained as sources for this review. Selection was based on relevance to control of powered prostheses. Summary: Significant strides have been made in expanding the choice of interface sites for bionic prosthesis control. Muscles, nerves, and the brain are all options, each with varying degrees of invasiveness and corresponding resolution of information obtained, and non-muscle interfacing prostheses may soon be commercially available. These advances have allowed for increasingly precise control of prosthetic limbs. However, this is limited by the challenge of returning sensory information from the prosthesis back to the user.

APA, Harvard, Vancouver, ISO, and other styles

6

Copeland, Christopher, Mukul Mukherjee, Yingying Wang, Kaitlin Fraser, and Jorge M. Zuniga. "Changes in Sensorimotor Cortical Activation in Children Using Prostheses and Prosthetic Simulators." Brain Sciences 11, no. 8 (July 27, 2021): 991. http://dx.doi.org/10.3390/brainsci11080991.

Full text

Abstract:

This study aimed to examine the neural responses of children using prostheses and prosthetic simulators to better elucidate the emulation abilities of the simulators. We utilized functional near-infrared spectroscopy (fNIRS) to evaluate the neural response in five children with a congenital upper limb reduction (ULR) using a body-powered prosthesis to complete a 60 s gross motor dexterity task. The ULR group was matched with five typically developing children (TD) using their non-preferred hand and a prosthetic simulator on the same hand. The ULR group had lower activation within the primary motor cortex (M1) and supplementary motor area (SMA) compared to the TD group, but nonsignificant differences in the primary somatosensory area (S1). Compared to using their non-preferred hand, the TD group exhibited significantly higher action in S1 when using the simulator, but nonsignificant differences in M1 and SMA. The non-significant differences in S1 activation between groups and the increased activation evoked by the simulator’s use may suggest rapid changes in feedback prioritization during tool use. We suggest that prosthetic simulators may elicit increased reliance on proprioceptive and tactile feedback during motor tasks. This knowledge may help to develop future prosthesis rehabilitative training or the improvement of tool-based skills.

APA, Harvard, Vancouver, ISO, and other styles

7

Richter, Claus-Peter, Andrew J. Fishman, and Agnella D. Izzo. "Cochlear Nerve Stimulation With Optical Radiation." Otolaryngology–Head and Neck Surgery 139, no. 2_suppl (August 2008): P99. http://dx.doi.org/10.1016/j.otohns.2008.05.519.

Full text

Abstract:

Problem Neural prosthetic devices are artificial extensions to the body that restore or supplement nervous system function that was lost during disease or injury. The devices stimulate remaining neural tissue with electric current, providing some input to the nervous system. Hereby, the challenge for neural prostheses is to stimulate remaining neurons selectively. However, electrical current spread does not easily allow stimulation of small neuron populations. In neural prostheses developments, particular success has been realized in the cochlear prostheses development. The devices bypass damaged hair cells in the auditory system by direct electrical stimulation of the auditory nerve. Stimulating discrete spiral ganglion cell populations in cochlear implant users’ ears is similar to the encoding of small acoustic frequency bands in a normal-hearing person's ear. In contemporary cochlear implants, however, the injected electric current is spread widely along the scala tympani and across turns. Consequently, stimulation of spatially discrete spiral ganglion cell populations is difficult. Methods Spiral ganglion cells in guinea pigs were stimulated with laser pulses from an Aculight Capella infrared laser. Results With our experiments we demonstrate that extreme spatially selective stimulation is possible using light. Conclusion Our long-term goal is to develop and build an optical cochlear implant prosthesis to stimulate small populations of spiral ganglion cells. Significance Our long-term goal is to develop and build an optical cochlear implant prosthesis to stimulate small populations of spiral ganglion cells. Support This project has been funded with federal funds from the National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN260-2006-00006-C / NIH No. N01-DC-6-0.

APA, Harvard, Vancouver, ISO, and other styles

8

Cunningham, John P., Paul Nuyujukian, Vikash Gilja, Cindy A. Chestek, Stephen I. Ryu, and Krishna V. Shenoy. "A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces." Journal of Neurophysiology 105, no. 4 (April 2011): 1932–49. http://dx.doi.org/10.1152/jn.00503.2010.

Full text

Abstract:

Neural prosthetic systems seek to improve the lives of severely disabled people by decoding neural activity into useful behavioral commands. These systems and their decoding algorithms are typically developed “offline,” using neural activity previously gathered from a healthy animal, and the decoded movement is then compared with the true movement that accompanied the recorded neural activity. However, this offline design and testing may neglect important features of a real prosthesis, most notably the critical role of feedback control, which enables the user to adjust neural activity while using the prosthesis. We hypothesize that understanding and optimally designing high-performance decoders require an experimental platform where humans are in closed-loop with the various candidate decode systems and algorithms. It remains unexplored the extent to which the subject can, for a particular decode system, algorithm, or parameter, engage feedback and other strategies to improve decode performance. Closed-loop testing may suggest different choices than offline analyses. Here we ask if a healthy human subject, using a closed-loop neural prosthesis driven by synthetic neural activity, can inform system design. We use this online prosthesis simulator (OPS) to optimize “online” decode performance based on a key parameter of a current state-of-the-art decode algorithm, the bin width of a Kalman filter. First, we show that offline and online analyses indeed suggest different parameter choices. Previous literature and our offline analyses agree that neural activity should be analyzed in bins of 100- to 300-ms width. OPS analysis, which incorporates feedback control, suggests that much shorter bin widths (25–50 ms) yield higher decode performance. Second, we confirm this surprising finding using a closed-loop rhesus monkey prosthetic system. These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients.

APA, Harvard, Vancouver, ISO, and other styles

9

LARYIONAVA, KATSIARYNA, and DOMINIK GROSS. "Public Understanding of Neural Prosthetics in Germany: Ethical, Social, and Cultural Challenges." Cambridge Quarterly of Healthcare Ethics 20, no. 3 (May 20, 2011): 434–39. http://dx.doi.org/10.1017/s0963180111000119.

Full text

Abstract:

Since the development of the first neural prosthesis, that is, the cochlear implant in 1957, neural prosthetics have been one of the highly promising, yet most challenging areas of medicine, while having become a clinically accepted form of invasiveness into the human body. Neural prosthetic devices, of which at least one part is inserted into the body, interact directly with the nervous system to restore or replace lost or damaged sensory, motor, or cognitive functions. This field is not homogenous and encompasses a variety of technologies, which are in various stages of development. Some devices are well established in clinical practice and have become routine, such as cochlear implants. By comparison, other technologies are in experimental phases and still need to be further developed to achieve the desired results.

APA, Harvard, Vancouver, ISO, and other styles

10

Bakay, Roy A. E., and Prasad S. S. V. Vannemreddy. "Neural Prosthesis: Concept and Progress." World Neurosurgery 78, no. 6 (December 2012): 576–78. http://dx.doi.org/10.1016/j.wneu.2011.10.023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chang, Ying, Lan Wang, Lingjie Lin, and Ming Liu. "Deep Neural Network for Electromyography Signal Classification via Wearable Sensors." International Journal of Distributed Systems and Technologies 13, no. 3 (July 1, 2022): 1–11. http://dx.doi.org/10.4018/ijdst.307988.

Full text

Abstract:

The human-computer interaction has been widely used in many fields, such intelligent prosthetic control, sports medicine, rehabilitation medicine, and clinical medicine. It has gradually become a research focus of social scientists. In the field of intelligent prosthesis, sEMG signal has become the most widely used control signal source because it is easy to obtain. The off-line sEMG control intelligent prosthesis needs to recognize the gestures to execute associated action. In order solve this issue, this paper adopts a CNN plus BiLSTM to automatically extract sEMG features and recognize the gestures. The CNN plus BiLSTM can overcome the drawbacks in the manual feature extraction methods. The experimental results show that the proposed gesture recognition framework can extract overall gesture features, which can improve the recognition rate.

APA, Harvard, Vancouver, ISO, and other styles

12

Valle, Giacomo, Albulena Saliji, Ezra Fogle, Andrea Cimolato, Francesco M. Petrini, and Stanisa Raspopovic. "Mechanisms of neuro-robotic prosthesis operation in leg amputees." Science Advances 7, no. 17 (April 2021): eabd8354. http://dx.doi.org/10.1126/sciadv.abd8354.

Full text

Abstract:

Above-knee amputees suffer the lack of sensory information, even while using most advanced prostheses. Restoring intraneural sensory feedback results in functional and cognitive benefits. It is unknown how this artificial feedback, restored through a neuro-robotic leg, influences users’ sensorimotor strategies and its implications for future wearable robotics. To unveil these mechanisms, we measured gait markers of a sensorized neuroprosthesis in two leg amputees during motor tasks of different difficulty. Novel sensorimotor strategies were intuitively promoted, allowing for a higher walking speed in both tasks. We objectively quantified the augmented prosthesis’ confidence and observed the reshaping of the legs’ kinematics toward a more physiological gait. In a possible scenario of a leg amputee driving a conventional car, we showed a finer pressure estimation from the prosthesis. Users exploited different features of the neural stimulation during tasks, suggesting that a simple prosthesis sensorization could be effective for future neuro-robotic prostheses.

APA, Harvard, Vancouver, ISO, and other styles

13

Ramírez-García, Alfredo, Lorenzo Leija, and Roberto Muñoz. "Active Upper Limb Prosthesis Based on Natural Movement Trajectories." Prosthetics and Orthotics International 34, no. 1 (March 2010): 58–72. http://dx.doi.org/10.3109/03093640903463792.

Full text

Abstract:

The motion of the current prostheses is sequential and does not allow natural movements. In this work, complex natural motion patterns from a healthy upper limb were characterized in order to be emulated for a trans-humeral prosthesis with three degrees of freedom at the elbow. Firstly, it was necessary to define the prosthesis workspace, which means to establish a relationship using an artificial neural network (ANN), between the arm-forearm (3-D) angles allowed by the prosthesis, and its actuators length. The 3-D angles were measured between the forearm and each axis of the reference system attached at the elbow. Secondly, five activities of daily living (ADLs) were analyzed by means of the elbow flexion (EF), the forearm prono-supination (FPS) and the 3-D angles, from healthy subjects, by using a video-based motion analysis system. The 3-D angles were fed to the prosthesis model (ANN) in order to analyze which ADLs could be emulated by the prosthesis. As a result, a prosthesis kinematics approximation was obtained. In conclusion, in spite of the innovative mechanical configuration of the actuators, it was possible to carry out only three of the five ADLs considered. Future work will include improvement of the mechanical configuration of the prosthesis to have greater range of motion.

APA, Harvard, Vancouver, ISO, and other styles

14

Dai, W. H., and Xiao Dong Zhang. "Design on the System of Brain-Computer Interface Driving Neural Prosthesis Hand." Key Engineering Materials 392-394 (October 2008): 1012–18. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.1012.

Full text

Abstract:

In this paper, the system of brain-computer interface driving neural prosthesis hand is designed using EEG signal as the control signal source. First, a whole design scheme of the system of BCI driving neural prosthesis hand is constructed and the method of EEG detection and recognition is introduced. Then, on the basis of prosthesis hand wildly used by amputees, a three-degree of freedom prosthesis hand is chosen as the control object through rebuilt according to system requirement. The serial communication channel is constructed and the recognition result of hand action is sent to the prosthesis hand controller through serial signal protocol. A prosthesis hand controller is designed using single-chip computer as its core, and the driving method of start-stop and steering of prosthesis hand motor is studied. At last, the purpose of the control of prosthesis hand is achieved by EEG recognition result from hand action.

APA, Harvard, Vancouver, ISO, and other styles

15

Broderick, Barry, Paul Breen, and Gearóid Ólaighin. "Electronic stimulators for surface neural prosthesis." Journal of Automatic Control 18, no. 2 (2008): 25–33. http://dx.doi.org/10.2298/jac0802025b.

Full text

Abstract:

This paper presents the technological advancements in neural prosthesis devices using Functional Electrical Stimulation (FES). FES refers to the restoration of motor functions lost due to spinal cord injury (SCI), stroke, head injury, or diseases such as Cerebral Palsy or Multiple Sclerosis by eliciting muscular contractions through the use of a neuromuscular electrical stimulator device. The field has developed considerably since its inception, with the miniaturisation of circuity, the development of programmable and adaptable stimulators and the enhancement of sensors used to trigger the application of stimulation to suit a variety of FES applications. This paper discusses general FES system design requirements in the context of existing commercial and research FES devices, focusing on surface stimulators for the upper and lower limbs. These devices have demonstrated feasible standing and stepping in a clinical setting with paraplegic patients, improvements in dropped foot syndrome with hemiplegic patients and aided in the restoration of grasping function in patients with upper limb motor dysfunction.

APA, Harvard, Vancouver, ISO, and other styles

16

Zhang, Xinglei, Binghui Fan, Chuanjiang Wang, Xiaolin Cheng, Hongguang Feng, and Zhaohui Tian. "Random Target Localization for an Upper Limb Prosthesis." Shock and Vibration 2021 (June 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/5297043.

Full text

Abstract:

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

APA, Harvard, Vancouver, ISO, and other styles

17

Mao, Lin, Xiao Lu, Chao Yu, and Kuiying Yin. "Physiological and Neural Changes with Rehabilitation Training in a 53-Year Amputee: A Case Study." Brain Sciences 12, no. 7 (June 26, 2022): 832. http://dx.doi.org/10.3390/brainsci12070832.

Full text

Abstract:

Many people who received amputation wear sEMG prostheses to assist in their daily lives. How these prostheses promote muscle growth and change neural activity remains elusive. We recruited a subject who had his left hand amputated for over 53 years to participate in a six-week rehabilitation training using an sEMG prosthesis. We tracked the muscle growth of his left forearm and changes in neural activity over six weeks. The subject showed an increase in fast muscle fiber in his left forearm during the training period. In an analysis of complex networks of neural activity, we observed that the α-band network decreased in efficiency but increased in its capability to integrate information. This could be due to an expansion of the network to accommodate new movements enabled by rehabilitation training. Differently, we found that in the β-band network, a band frequency related to motor functions, the efficiency of the network initially decreased but started to increase after approximately three weeks. The ability to integrate network information showed an opposite trend compared with its efficiency. rMT values, a measure that negatively correlates with cortical excitability, showed a sharp decrease in the first three weeks, suggesting an increase in cortical excitability. In the last three weeks, there was little to no change. These data indicate that rehabilitation training promoted fast muscle fiber growth and introduced neural activity changes in the subject during the first three weeks of training. Our study gave insights into how rehabilitation training with an sEMG prosthesis could lead to physiological and neural changes in amputees.

APA, Harvard, Vancouver, ISO, and other styles

18

Markowitz, Jared, Pavitra Krishnaswamy, Michael F. Eilenberg, Ken Endo, Chris Barnhart, and Hugh Herr. "Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1570 (May 27, 2011): 1621–31. http://dx.doi.org/10.1098/rstb.2010.0347.

Full text

Abstract:

Control schemes for powered ankle–foot prostheses would benefit greatly from a means to make them inherently adaptive to different walking speeds. Towards this goal, one may attempt to emulate the intact human ankle, as it is capable of seamless adaptation. Human locomotion is governed by the interplay among legged dynamics, morphology and neural control including spinal reflexes. It has been suggested that reflexes contribute to the changes in ankle joint dynamics that correspond to walking at different speeds. Here, we use a data-driven muscle–tendon model that produces estimates of the activation, force, length and velocity of the major muscles spanning the ankle to derive local feedback loops that may be critical in the control of those muscles during walking. This purely reflexive approach ignores sources of non-reflexive neural drive and does not necessarily reflect the biological control scheme, yet can still closely reproduce the muscle dynamics estimated from biological data. The resulting neuromuscular model was applied to control a powered ankle–foot prosthesis and tested by an amputee walking at three speeds. The controller produced speed-adaptive behaviour; net ankle work increased with walking speed, highlighting the benefits of applying neuromuscular principles in the control of adaptive prosthetic limbs.

APA, Harvard, Vancouver, ISO, and other styles

19

Sattar, Neelum Yousaf, Zareena Kausar, Syed Ali Usama, Umer Farooq, Muhammad Faizan Shah, Shaheer Muhammad, Razaullah Khan, and Mohamed Badran. "fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees." Sensors 22, no. 3 (January 18, 2022): 726. http://dx.doi.org/10.3390/s22030726.

Full text

Abstract:

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

APA, Harvard, Vancouver, ISO, and other styles

20

Yan, Gongxing, Jialing Li, Hui Xie, and Minggui Zhou. "5G Virtual Reality System for Prosthetic Wearer Gait Evaluation and Application in Intelligent Prosthesis Debugging." Mobile Information Systems 2022 (September 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/6311065.

Full text

Abstract:

The main purpose of developing intelligent lower limb prostheses is to improve the quality of life of the disabled and promote the development of medical care. At the same time, the research of robotic intelligent lower limb prosthesis is a research direction that has been widely concerned in the field of robotics and biomedical engineering technology in recent years. This paper aims to research and discuss the 5G virtual reality system based on the gait evaluation of prosthetic wearers and its application in the debugging of intelligent prostheses. First, this paper analyzes the combination of the concept of lower extremity rehabilitation and virtual reality. Virtual reality technology is a means of exercise rehabilitation for patients with lower extremity hemiplegia, which plays an auxiliary role in the rehabilitation process. Then, the recognition and pre-method of human lower limb motion state are described. In the field of intelligent prosthetic state recognition, in addition to neural network recognition methods, there are hidden Markov (HMM) recognition methods, auxiliary vector machines (SVM), and so on. Finally, the lower limb state phase detection experiment and the prosthetic control experiment based on state phase detection are studied. The results of this experimental study showed that, for the same experimenter, changes in pace and gait did not affect the percentage of standing and swing phases. That is, the standing phase accounts for about 60% of the entire gait cycle, while the swing phase accounts for 40% of the entire gait cycle. This rule has been verified in other experiments.

APA, Harvard, Vancouver, ISO, and other styles

21

Gibas, Christian, Luca Mülln, and Rainer Brück. "Use of artificial intelligence and neural networks for analysis and gesture detection in electrical impedance tomography." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 489–92. http://dx.doi.org/10.1515/cdbme-2020-3126.

Full text

Abstract:

AbstractArtificial intelligence and neural networks are getting more and more relevant for several types of application. The field of prosthesis technology currently uses electromyography for controllable prosthesis. The precision of the control suffers from the use of EMG. More precise and more collected data with the help of EIT allows a much more precise analysis and control of the prosthesis. In this paper a neural network for gesture detection using EIT is developed and presented in a user-friendly way.

APA, Harvard, Vancouver, ISO, and other styles

22

Caravaca-Rodriguez, Daniel, Susana P. Gaytan, Gregg J. Suaning, and Alejandro Barriga-Rivera. "Implications of Neural Plasticity in Retinal Prosthesis." Investigative Opthalmology & Visual Science 63, no. 11 (October 17, 2022): 11. http://dx.doi.org/10.1167/iovs.63.11.11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Guenther, Frank H. "Real‐time speech synthesis for neural prosthesis." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2496. http://dx.doi.org/10.1121/1.4783342.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Hepp, D., J. Kirsch, and F. Capanni. "Smartphone supported upper limb prosthesis." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 484–87. http://dx.doi.org/10.1515/cdbme-2015-0116.

Full text

Abstract:

AbstractState of the art upper limb prostheses offer up to six active DoFs (degrees of freedom) and are controlled using different grip patterns. This low number of DoFs combined with a machine-human-interface which does not provide control over all DoFs separately result in a lack of usability for the patient. The aim of this novel upper limb prosthesis is both offering simplified control possibilities for changing grip patterns depending on the patients’ priorities and the improvement of grasp capability. Design development followed the design process requirements given by the European Medical Device Directive 93/42 ECC and was structured into the topics mechanics, software and drive technology. First user needs were identified by literature research and by patient feedback. Consequently, concepts were evaluated against technical and usability requirements. A first evaluation prototype with one active DoF per finger was manufactured. In a second step a test setup with two active DoF per finger was designed. The prototype is connected to an Android based smartphone application. Two main grip patterns can be preselected in the software application and afterwards changed and used by the EMG signal. Three different control algorithms can be selected: “all-day”, “fine” and “tired muscle”. Further parameters can be adjusted to customize the prosthesis to the patients’ needs. First patient feedback certified the prosthesis an improved level of handling compared to the existing devices. Using the two DoF test setup, the possibilities of finger control with a neural network are evaluated at the moment. In a first user feedback test, the smartphone based software application increased the device usability, e.g. the change within preselected grip patterns and the “tired muscle” algorithm. Although the overall software application was positively rated, the handling of the prosthesis itself needs to be proven within a patient study to be performed next. The capability of the neural network to control the hand has also to be proven in a next step.

APA, Harvard, Vancouver, ISO, and other styles

25

Gini, Giuseppina, Matteo Arvetti, Ian Somlai, and Michele Folgheraiter. "Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications." Applied Bionics and Biomechanics 9, no. 2 (2012): 145–55. http://dx.doi.org/10.1155/2012/792359.

Full text

Abstract:

One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.

APA, Harvard, Vancouver, ISO, and other styles

26

Lin, Chin-Yu, Wan-Shiun Lou, Jyh-Chern Chen, Kuo-Yao Weng, Ming-Cheng Shih, Ya-Wen Hung, Zhu-Yin Chen, and Mei-Chih Wang. "Bio-Compatibility and Bio-Insulation of Implantable Electrode Prosthesis Ameliorated by A-174 Silane Primed Parylene-C Deposited Embedment." Micromachines 11, no. 12 (November 30, 2020): 1064. http://dx.doi.org/10.3390/mi11121064.

Full text

Abstract:

Microelectrodes for pain management, neural prosthesis or assistances have a huge medical demand, such as the application of pain management chip or retinal prosthesis addressed on age-related macular degeneration (AMD) and the retinitis pigmentosa (RP). Due to lifelong implanted in human body and direct adhesion of neural tissues, the electrodes and associated insulation materials should possess an ideal bio-compatibility, including non-cytotoxicity and no safety concern elicited by immune responses. Our goal intended to develop retinal prosthesis, an electrical circuit chip used for assisting neural electrons transmission on retina and ameliorating the retinal disability. Therefore, based on the ISO 10993 guidance for implantable medical devices, the electrode prosthesis with insulation material has to conduct bio-compatibility assessment including cytotoxicity, hemolysis, (skin) irritation and pathological implantation examinations. In this study, we manufactured inter-digitated electrode (IDE) chips mimic the electrode prosthesis through photolithography. The titanium and platinum composites were deposited onto a silicon wafer to prepare an electric circuit to mimic the electrode used in retinal prosthesis manufacture, which further be encapsulated to examine the bio-compatibility in compliance with ISO 10993 and ASTM guidance specifically for implantable medical devices. Parylene-C, polyimide and silicon carbide were selected as materials for electrode encapsulation in comparison. Our data revealed parylene-C coating showed a significant excellence on bio-insulation and bio-compatibility specifically addressed on implantable neuron stimulatory devices and provided an economic procedure to package the electrode prosthesis. Therefore, parylene C encapsulation should serve as a consideration for future application on retinal prosthesis manufacture and examination.

APA, Harvard, Vancouver, ISO, and other styles

27

Davoodi, Rahman, and Gerald E. Loeb. "Development of a Physics-Based Target Shooting Game to Train Amputee Users of Multijoint Upper Limb Prostheses." Presence: Teleoperators and Virtual Environments 21, no. 1 (February 2012): 85–95. http://dx.doi.org/10.1162/pres_a_00091.

Full text

Abstract:

For upper limb amputees, learning the control of myoelectric prostheses is difficult and challenging. Introduction of newer prostheses with multiple degrees of freedom controlled by various neural commands will make such training even more difficult. To produce smooth and human-like movements, the user must learn to produce multiple neural commands with precise amplitude and timing. To aid in training of the amputee users, we have developed a realistic and motivating virtual environment (VE) consisting of a physics-based target shooting game. The users' neural commands such as EMG, cortical neural activity, or voluntary movements of the residual limbs can be used to control the movement of a simulated prosthesis to point and shoot at virtual targets. In addition to the visual, sound, and performance feedback of the resulting movement, the game provides reaction forces in contact points that can be used to drive haptic displays. The timing measurements show that the physics-based simulation and rendering can be executed in real time in readily available PC systems. The target shooting game was developed in musculoskeletal modeling software (MSMS) that has been developed in our laboratory and is freely available for development of similar virtual training applications.

APA, Harvard, Vancouver, ISO, and other styles

28

BARRETT, JOHN MARTIN, ROLANDO BERLINGUER-PALMINI, and PATRICK DEGENAAR. "Optogenetic approaches to retinal prosthesis." Visual Neuroscience 31, no. 4-5 (August 6, 2014): 345–54. http://dx.doi.org/10.1017/s0952523814000212.

Full text

Abstract:

AbstractThe concept of visual restoration via retinal prosthesis arguably started in 1992 with the discovery that some of the retinal cells were still intact in those with the retinitis pigmentosa disease. Two decades later, the first commercially available devices have the capability to allow users to identify basic shapes. Such devices are still very far from returning vision beyond the legal blindness. Thus, there is considerable continued development of electrode materials, and structures and electronic control mechanisms to increase both resolution and contrast. In parallel, the field of optogenetics—the genetic photosensitization of neural tissue holds particular promise for new approaches. Given that the eye is transparent, photosensitizing remaining neural layers of the eye and illuminating from the outside could prove to be less invasive, cheaper, and more effective than present approaches. As we move toward human trials in the coming years, this review explores the core technological and biological challenges related to the gene therapy and the high radiance optical stimulation requirement.

APA, Harvard, Vancouver, ISO, and other styles

29

Sinkjaer, Thomas. "Integrating Sensory Nerve Signals Into Neural Prosthesis Devices." Neuromodulation: Technology at the Neural Interface 3, no. 1 (January 2000): 34–41. http://dx.doi.org/10.1046/j.1525-1403.2000.00035.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Gilja, Vikash, Chethan Pandarinath, Christine H. Blabe, Paul Nuyujukian, John D. Simeral, Anish A. Sarma, Brittany L. Sorice, et al. "Clinical translation of a high-performance neural prosthesis." Nature Medicine 21, no. 10 (September 28, 2015): 1142–45. http://dx.doi.org/10.1038/nm.3953.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Ionescu, C. M., and R. M. C. De Keyser. "Control of paralyzed skeletal muscles: A neural prosthesis." Computer Methods in Biomechanics and Biomedical Engineering 8, sup1 (September 2005): 145–46. http://dx.doi.org/10.1080/10255840512331388678.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Normann, Richard A., Edwin M. Maynard, Patrick J. Rousche, and David J. Warren. "A neural interface for a cortical vision prosthesis." Vision Research 39, no. 15 (July 1999): 2577–87. http://dx.doi.org/10.1016/s0042-6989(99)00040-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Hussain, Jabbar Salman, Ahmed Al-Khazzar, and Mithaq Nama Raheema. "Recognition of additional myo armband gestures for myoelectric prosthetic applications." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5694. http://dx.doi.org/10.11591/ijece.v10i6.pp5694-5702.

Full text

Abstract:

Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and ﬁnger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (ﬁve build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.

APA, Harvard, Vancouver, ISO, and other styles

34

Sun, Baofeng, and Wanzhong Chen. "CLASSIFICATION OF sEMG SIGNALS USING INTEGRATED NEURAL NETWORK WITH SMALL SIZED TRAINING DATA." Biomedical Engineering: Applications, Basis and Communications 24, no. 04 (August 2012): 365–76. http://dx.doi.org/10.4015/s1016237212500329.

Full text

Abstract:

The sEMG (Surface electromyography) signals detected from activated muscles can be used as a control source for prosthesis. So an efficient and accurate method for the classification of sEMG signal patterns has become a hot research in recent years. Artificial neural network is a popular used method in this field, however, most neural networks require large numbers of samples in the training stage to obtain the potential relationships between input feature vectors and the outputs. In this paper, Integrated back propagation neural network (IBPNN) is used to classify sEMG signals acquired during five different hand motions. The correct classification rates of IBPNN for the five hand movements are significantly higher than that of BPNN and Elman neural network. This reveals that IBPNN achieves the best performance with a small sized training data and can be used in control systems on prosthetic hands and other robotic devices based on electromyography pattern recognition.

APA, Harvard, Vancouver, ISO, and other styles

35

Murray, Rosemarie, Joel Mendez, Lukas Gabert, Nicholas P. Fey, Honghai Liu, and Tommaso Lenzi. "Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks." Sensors 22, no. 23 (December 1, 2022): 9350. http://dx.doi.org/10.3390/s22239350.

Full text

Abstract:

Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial–temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user’s gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Will X. Y., Ray C. C. Cheung, Yao Xin, Dong Song, and Theodore W. Berger. "An FPGA-Based High-Performance Neural Ensemble Spiking Activity Simulator Utilizing Generalized Volterra Kernel and Complexity Analysis." Journal of Circuits, Systems and Computers 25, no. 01 (November 15, 2015): 1640004. http://dx.doi.org/10.1142/s0218126616400041.

Full text

Abstract:

Neural information is represented and transmitted among neuronal units by a series of all-or-none “neural codes”. During the process of neural prosthesis design, generally, a large amount of “neural codes” need to be captured and analyzed, which brings about an important discipline, known as neuroinformatics. However, in neuroinformatics study, this coding process, also termed as “spiking activity”, is not straightforward for prediction. It is owing to the high nonlinearity and dynamic property involved in generation of the neuronal spikes. In this paper, a novel generalized Volterra kernel-based neural spiking activity simulator is introduced for prediction of the neural codes in mammalian hippocampal region. High-performance VLSI architecture is established for the simulator based on high-order Volterra kernels involving cross-terms. The effectiveness and efficiency of the simulator are proven in experimental settings. This simulator has the potential to serve as a core functional unit in future hippocampal cognitive neural prosthesis.

APA, Harvard, Vancouver, ISO, and other styles

37

Buss, Stephanie. "From Visual Plasticity to the Bionic Eye." Einstein Journal of Biology and Medicine 27, no. 1 (March 2, 2016): 10. http://dx.doi.org/10.23861/ejbm20112725.

Full text

Abstract:

While visual plasticity is strongest in early infancy, new studies show that plasticity is maintained well into adult life.This possibility is compellingly demonstrated by one patient, SK, who gained vision for the first time in adult life andsignificantly improved his ability to see the world around him. The persistence of visual plasticity in adults is promisingnews for the developing field of visual prosthesis.In recent years, there has been an explosion of research on prosthetic devices for the brain. While memory-enhancingbrain chips are still science fiction, cochlear implants, which stimulate the inner ear with tiny electrodes, now allowpeople who were once deaf to hear with increasing accuracy. Although there is not yet any visual equivalent to thecochlear implant, in recent years vision researchers have started to experiment with similar prosthetic techniques totreat blindness.The goal of visual prosthesis is to allow functional restoration of vision and to improve quality of life for blindpatients. In order to achieve these goals, the prosthetic devices must tap into the brain’s plasticity. Plasticity is howthe brain adapts to new environmental stimuli. It enables all forms of learning, including memorizing facts, playingthe piano, and learning to see. Specifically, plasticity is how neural networks in the brain reorganize in response tonew experiences. Understanding plasticity furthers insight into the brain mechanisms active in visual prostheses, andmay help scientists develop new approaches for future devices.

APA, Harvard, Vancouver, ISO, and other styles

38

Jothi Lakshmi, D., G. Illakiya, and R. Rajkamal. "A Novel Approach and Design of Embedded Controlled Prosthetic Upper Limb to Assist the above Elbow Amputees." Advanced Materials Research 403-408 (November 2011): 2039–45. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2039.

Full text

Abstract:

The existing prosthetic upper limb design and control is divided into two broad categories. One is the myoelectric prosthesis where electromechanical active joints actuate the arm segments and is directly activated by acquiring Electromyogram (EMG) signals from the amputee which is sensed by myoelectric electrodes. Acquiring of the EMG signals is a tedious process as it involves adequate amplification and proper filtering. Also isolation of noise from EMG signals poses difficulty. The other category falls under intelligent prosthetic hand where neural networks (NN) are involved. It requires adequate training for NN operation that leads to the complexity in implementing electronic circuits. The major disadvantage of the above mentioned technologies is lack of proprioceptive feedback from the amputee. The drawbacks of the existing technologies motivates us to design a prototype with proprioceptive feedback to control the Above Elbow (AE) prosthesis with a permanent magnet implanted at the distal end of the residual humerus of the amputee. The proprioception remains intact to the residual limb skeletal structure. In this work, the proposed approach involves in processing the magnetic field variation due to residual arm bone movement which is sensed by magnetic field sensors. The embedded controller controls the movements of the prosthetic hand by processing the signals received from the sensors to assist the AE amputee.

APA, Harvard, Vancouver, ISO, and other styles

39

Emayavaramban, G., A. Amudha, T. Rajendran, M. Sivaramkumar, K. Balachandar, and T. Ramesh. "Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks." Current Signal Transduction Therapy 14, no. 2 (October 10, 2019): 158–64. http://dx.doi.org/10.2174/1574362413666180604100542.

Full text

Abstract:

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

APA, Harvard, Vancouver, ISO, and other styles

40

Lai, Qiuxia, Dingyin Hu, Ang Ke, and Jiping He. "Providing Sensory Feedback Using Electrical Stimulation for Neural Prosthesis." Neuroscience and Biomedical Engineering 2, no. 2 (April 10, 2015): 99–104. http://dx.doi.org/10.2174/2213385203666150328002141.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Gong, Wangsong, and Daniel M. Merfeld. "Prototype Neural Semicircular Canal Prosthesis using Patterned Electrical Stimulation." Annals of Biomedical Engineering 28, no. 5 (May 2000): 572–81. http://dx.doi.org/10.1114/1.293.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Troyk, P., and Zhe Hu. "Simplified Design Equations for Class-E Neural Prosthesis Transmitters." IEEE Transactions on Biomedical Engineering 60, no. 5 (May 2013): 1414–21. http://dx.doi.org/10.1109/tbme.2012.2237172.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Purcell, E. K., J. P. Seymour, S. Yandamuri, and D. R. Kipke. "In vivoevaluation of a neural stem cell-seeded prosthesis." Journal of Neural Engineering 6, no. 4 (July 22, 2009): 049801. http://dx.doi.org/10.1088/1741-2552/6/4/049801.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Hageman, Kristin N., Zaven K. Kalayjian, Francisco Tejada, Bryce Chiang, Mehdi A. Rahman, Gene Y. Fridman, Chenkai Dai, et al. "A CMOS Neural Interface for a Multichannel Vestibular Prosthesis." IEEE Transactions on Biomedical Circuits and Systems 10, no. 2 (April 2016): 269–79. http://dx.doi.org/10.1109/tbcas.2015.2409797.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Kalanovic, Vojislav D., and Nils T. Skaug. "Feedback Error Learning Neural Network for Above-Knee Prosthesis." IFAC Proceedings Volumes 30, no. 6 (May 1997): 1617–22. http://dx.doi.org/10.1016/s1474-6670(17)43592-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Purcell, E. K., J. P. Seymour, S. Yandamuri, and D. R. Kipke. "In vivoevaluation of a neural stem cell-seeded prosthesis." Journal of Neural Engineering 6, no. 2 (March 13, 2009): 026005. http://dx.doi.org/10.1088/1741-2560/6/2/026005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Seligman, Peter. "Prototype to product—developing a commercially viable neural prosthesis." Journal of Neural Engineering 6, no. 6 (October 23, 2009): 065006. http://dx.doi.org/10.1088/1741-2560/6/6/065006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Berger, Theodore W., Robert E. Hampson, Dong Song, Anushka Goonawardena, Vasilis Z. Marmarelis, and Sam A. Deadwyler. "A cortical neural prosthesis for restoring and enhancing memory." Journal of Neural Engineering 8, no. 4 (June 15, 2011): 046017. http://dx.doi.org/10.1088/1741-2560/8/4/046017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Kalanovic, V. D., D. Popovic, and N. T. Skaug. "Feedback error learning neural network for trans-femoral prosthesis." IEEE Transactions on Rehabilitation Engineering 8, no. 1 (March 2000): 71–80. http://dx.doi.org/10.1109/86.830951.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Miler, Vera, Goran Bijelic, and Laszlo Schwirtlich. "Neural prosthesis for the therapy of low back pain." Journal of Automatic Control 18, no. 2 (2008): 93–97. http://dx.doi.org/10.2298/jac0802093m.

Full text

Abstract:

We present methods to treat Low Back Pain (LBP) with electrical stimulation. The conventional method of treating LBP with electrical stimulation is based on mechanisms of reduction of pain. The method that we suggest uses electrical stimulation of trunk muscles at motor suprathreshold level synchronized with exercising of the trunk muscles. The hypothesis was that the combination of voluntary activity augmented with electrical stimulation would lead to higher levels of recovery of postural control and thereby, reduction of LBP. The electrical stimulation in this treatment was delivered with the lumbar belt with eight pairs of electrodes named Stimbelt. The outcome measures included: a Visual Analogue Scale (VAS), the Oswestry LBP disability questionnaire, the SF-12 health survey, and Manual Muscle Test (MMT). We specifically address the selection of the most appropriate statistical tests for the analysis of results. The analysis of the results of the clinical study indicates significant benefits of the addition of the Stimbelt to the conventional therapy.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Neural prosthesis'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles