Compliant grasp in a myoelectric hand prosthesis

A novel myoelectric hand prosthesis consisting of electromyogram (EMG) signal processing units, a microprocessor-based dc motor servo system, and a 1 degree-of-freedom (DOF) end effector has been developed. The flexion angle and compliance of the finger of this prosthesis can be voluntarily controlled with EMG signals. The biomimetic controller for the myoelectric hand incorporated a model of the neuromuscular control system constructed from an analysis of the force response to length perturbation of the flexor pollicis longus muscle, processing of EMG signals, and the configuration of the hand. Basic functions of the human neuromuscular control system are realized by using position control, force feedback, and variable gain, modulated by EMG signal amplitude. A limb-absent person and four healthy subjects were able to voluntarily control the finger angle and compliance of the prosthesis and were able to easily grasp a soft object after a short training period.     

Mechatronic experiments course design: a myoelectric controlled partial-hand prosthesis project

This paper describes a proposed laboratory project involving the design of a myoelectric-controlled partial-hand prosthesis to reinforce mechatronic education. The proposal focuses mainly on extract electromyogram (EMG) signals generated during contraction of the biceps. The EMG signals are first amplified and filtered by a laboratory-designed electronic circuit and then reprocessed using a microcontroller to drive the servomotor so that the designed prosthesis can be properly controlled. The project introduces students to component-level and system-level design and exposes them to the integration of a microcontroller, electronic circuits, sensors, and prosthesis mechanisms. Moreover, since the project results in a working prosthesis, student enthusiasm for mechatronic education increases, and they see its relevance to the field in applied engineering. Implementation of the laboratory project within the curriculum has been demonstrated to be highly motivational and educational and has even helped to attract more students to study mechatronic applications.     

An accurate forearm EMG signal classification method using two‐channel electrode

An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind‐source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen‐based feature that is extracted from the short‐time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well‐known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Cortical neural prosthesis performance improves when eye position is monitored.

Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve prosthesis performance is to account for the direction of gaze in the operation of the prosthesis. This proposal stems from recent discoveries that the direction of gaze influences neural activity in several areas that are commonly targeted for electrode implantation in neural prosthetics. Here, we first demonstrate that neural prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from neural activity, and thus performance gains can be realized even without a device that tracks eye position.     

Improved myoelectric prosthesis control using targeted reinnervation surgery: a case series.

Targeted reinnervation is a surgical technique developed to increase the number of myoelectric input sites available to control an upper-limb prosthesis. Because signals from the nerves related to specific movements are used to control those missing degrees-of-freedom, the control of a prosthesis using this procedure is more physiologically appropriate compared to conventional control. This procedure has successfully been performed on three people with a shoulder disarticulation level amputation and three people with a transhumeral level amputation. Performance on timed tests, including the box-and-blocks test and clothespin test, has increased two to six times. Options for new control strategies are discussed.     

Fuzzy EMG classification for prosthesis control.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.     

Connectivity analysis as a novel approach to motor decoding for prosthesis control

The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r(2)) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.     

基于小波能谱的电力暂态信号分类识别方法

为区分故障性暂态信号与非故障性暂态信号,在研究电力系统各种暂态信号及其特点的基础上,建立了500kV输电线路EMTDC仿真模型,并产生了单相断路器操作、电容投切、接地短路、一次电弧故障、非故障性雷击和故障性雷击6种类型的暂态信号。利用多分辨分析的小波变换提取上述各暂态信号的频带能量特征和局部能量特征,总结得到了能量统计图。分析了各暂态信号的能量分布特点,并从保护的角度出发提出了暂态信号分类识别的判据。大量的仿真试验验证了该方法的可行性和有效性。     

Balance prostheses for postural control

There is a clear need for a prosthesis that improves postural stability in the balance impaired. Such a device would be used as a temporary aid during recovery from ablative inner-ear surgery and as a permanent prosthesis for those elderly prone to falls. Research using a one-axis device that estimates body tilt and displays it to vestibulopathic subjects via an array of tactile vibrators has demonstrated feasibility. The noninvasive, vibrotactile display of body tilt helped the balance-impaired subjects to reduce their body sway during standardized tests. The motion sensor array is comprised of three MEMS linear accelerometers and three MEMS rate gyros whose sensitive axes are aligned along three orthogonal directions to provide six-degree-of-freedom (dof) motion information.     

SPOC-a stored program optical computer

The Digital Optical Computing (DOC) group at the University of Colorado at Boulder have built the world's first stored program optical computer (SPOC). Several features that distinguish this computer from traditional electronic computers are: the use of optical fibers and pulses of light instead of wires and electronic signals; the use of space and time to store information instead of flip-flops; synchronization based on the predictable propagation time of signals; and return to zero signal encoding. One goal was to show that optics could be used to build a general purpose stored program computer. Another goal was to demonstrate that predictable signal propagation time could replace flip flops for synchronization. The SPOC is a functionally complete computer. The constant speed of light was used to synchronize the signals in the SPOC. We adjusted the length of the input fibers to the logic gates so the signals would arrive at the same time. This synchronization technique is called time-of-flight design. The group developed a design tool, called XHatch, to help design time-of-flight circuits. We used XHatch to determine the lengths of the optical fibers for the SPOC based on its circuit design. The fibers act as registers by storing the signals in space and time until they are fed into a logic element     

Analysis of electrocardiograms during atrial fibrillation

The research discussed in this article is motivated by the search for an optimal classification of the different types of atrial fibrillation (AF) on the basis of recorded atrial signals. This would facilitate the selection of an optimal therapy. This article focuses on the biophysical models of the genesis of ECG waveforms during AF. The model of the electric activity of the atria was found to have sufficient realism to be used to describe the electric sources during AF. The inclusion of the volume conduction model resulted in electrocardiographic signals that are in all aspects similar to those observed clinically. The model is currently applied to solve various problems related to optimal signal preprocessing and extraction of features in AF signals for the classification of AF abnormalities. The biophysical model of the atrial activity is an essential element in this research, since it is capable of describing the electric source specifications derived from different hypotheses relating to the etiology of AF     

采用Fisher线性判别法提取GIS内部局部放电信号最优能量特征

采用二元树复小波变换（DT-CWT）对特高频局部放电（PD）信号进行多尺度分解,求解了复小波最优分解层数,提取了最优分解尺度下的特高频 PD信号实部和虚部高频层小波能量,并采用Fisher线性判别方法对能量特征进行选择,最后进行PD类型辨识。识别结果表明：优选后的实部和虚部高频层小波能量特征可以有效识别4种典型绝缘缺陷,识别率均达到了92.5%及以上,且最优复小波能量（OCWEF）特征在PD类型辨识中具有更优的敏感性和识别效果。     

基于实测数据的电力系统过电压分类识别

构造了一个有效的基于实测数据的过电压自动分类识别树。首先抽取过电压信号的时域特征量,将过电压类别集合分为2个子集。其次对信号进行离散小波变换,抽取小波变换域特征量。为使小波变换域特征量更具区别性,对2个子集内的过电压信号采用不同的采样频率和小波分解层数。最后在分类树的各节点构造一个支持向量机二值分类器,采用实测过电压数据进行交叉验证。总识别率达95%,验证了分类树的有效性。     

基于小波变换的脑电高阶奇异谱分析

奇异谱分析是数字信号分析的一种新方法。信号的奇异谱反映信号的奇异特征。但奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。因而很难反映非线性信号内在的非线性关系。本文提出一种基于小波变换和高阶统计的奇异谱分析的新方法,并将其运用于正常脑电和癫痫患者的脑电信号分析中。实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。     

A Retinal Prosthesis Technology Based on CMOS Microelectronics and Microwire Glass Electrodes

A very large format neural stimulator device, to be used in future retinal prosthesis experiments, has been designed, fabricated, and tested. The device was designed to be positioned against a human retina for short periods in an operating room environment. Demonstrating a very large format, parallel interface between a 2-D microelectronic stimulator array and neural tissue would be an important step in proving the feasibility of high resolution retinal prosthesis for the blind. The architecture of the test device combines several novel components, including microwire glass, a microelectronic multiplexer, and a microcable connector. The array format is 80 times 40 array pixels with approximately 20 microwire electrodes per pixel. The custom assembly techniques involve indium bump bonding, ribbon bonding, and encapsulation. The design, fabrication, and testing of the device has resolved several important issues regarding the feasibility of high-resolution retinal prosthesis, namely, that the combination of conventional CMOS electronics and microwire glass provides a viable approach for a high resolution retinal prosthesis device. Temperature change from power dissipation within the device and maximum electrical output current levels suggest that the device is acceptable for acute human tests     

A CMOS multichannel electrical stimulation prototype system

We developed an inductively powered integrated electronic prosthesis, allowing for the trade‐offs among implant functionality, circuit complexity, power consumption, hardware cost, and integrity of data recovery, for a multichannel microstimulation circuitry. The proposed prosthesis features energy efficiency and is capable of up to 40 scan/s with 240 stimulus channels in mode I and three times resolution at the same scan rate in mode II under a carrier frequency of 2 MHz. In order to satisfy future upgrade demands, the prototype has been constructed with a 16‐channel‐based stimulation scheme so that the spatial resolution of the design can be extended toward various experimental purposes. The circuit techniques used in the system are detailed. Results from fabricated chips using a 0.18‐µm CMOS process are given as proof of concept. Copyright © 2011 John Wiley & Sons, Ltd.     

Restoring lost cognitive function.

A prosthetic device that functions in a biomimetic manner to replace information transmission between cortical brain regions is considered. In such a prosthesis, damaged CNS neurons is replaced with a biomimetic system comprised of silicon neurons. The replacement silicon neurons would have functional properties specific to those of the damaged neurons and would both receive as inputs and send as outputs electrical activity to regions of the brain with which the damaged region previously communicated. Thus, the class of prosthesis proposed is one that would replace the computational function of the damaged brain and restore the transmission of that computational result to other regions of the nervous system.     

Conveying Tactile Feedback in Sensorized Hand Neuroprostheses Using a Biofidelic Model of Mechanotransduction

One approach to conveying tactile feedback from sensorized neural prostheses is to characterize the neural signals that would normally be produced in an intact limb and reproduce them through electrical stimulation of the residual peripheral nerves. Toward this end, we have developed a model that accurately replicates the neural activity evoked by any dynamic stimulus in the three types of mechanoreceptive afferents that innervate the glabrous skin of the hand. The model takes as input the position of the stimulus as a function of time, along with its first (velocity), second (acceleration), and third (jerk) derivatives. This input is filtered and passed through an integrate-and-fire mechanism to generate a train of spikes as output. The major conclusion of this study is that the timing of individual spikes evoked in mechanoreceptive fibers innervating the hand can be accurately predicted by this model. We discuss how this model can be integrated in a sensorized prosthesis and show that the activity in a population of simulated afferents conveys information about the location, timing, and magnitude of contact between the hand and an object.      

A time-series prediction approach for feature extraction in a brain-computer interface.

This paper presents a feature extraction procedure (FEP) for a brain-computer interface (BCI) application where features are extracted from the electroencephalogram (EEG) recorded from subjects performing right and left motor imagery. Two neural networks (NNs) are trained to perform one-step-ahead predictions for the EEG time-series data, where one NN is trained on right motor imagery and the other on left motor imagery. Features are derived from the power (mean squared) of the prediction error or the power of the predicted signals. All features are calculated from a window through which all predicted signals pass. Separability of features is achieved due to the morphological differences of the EEG signals and each NNs specialization to the type of data on which it is trained. Linear discriminant analysis (LDA) is used for classification. This FEP is tested on three subjects off-line and classification accuracy (CA) rates range between 88% and 98%. The approach compares favorably to a well-known adaptive autoregressive (AAR) FEP and also a linear AAR model based prediction approach.