Neuromuscular Interfacing: Establishing an EMG-Driven Model for the Human Elbow Joint

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.     

Measuring human–robot interaction on wearable robots: A distributed approach

This paper presents a novel, distributed approach to monitor physical interaction between a user and a wearable robot. We propose to apply a matrix of optoelectronic sensors embedded in a thin and compliant silicone bulk onto the user-robot contact surface. This distributed tactile sensor can measure the pressure distribution on the interaction area without affecting the comfort of the user, and does not require the robot to be specifically designed to house it. Besides the estimation of the interaction force/torque, the distributed approach allows to monitor the pressure on the user’s skin. This information is fundamental to assess the comfort and safety of the users which determine the final acceptability of the robot-mediated rehabilitation. The proposed method is preliminary evaluated on an elbow active orthosis during a repetitive rehabilitation task. Experimental results prove the relevance of this approach for the detection of the user motion intention through a measurement of the interaction force distribution.     

The effects of noise in oscillators

An explicit expression for the output signal from an oscillator with several noise sources in the circuit is derived. This formula describes qualitatively and quantitatively the manner in which thermal and shot noise act to corrupt the performance of an ideal oscillator. The statistical properties of the signal are then evaluated, as it emerges from the oscillator stage, after passage through an output filter and after being operated on by an ideal n-times multiplier. Expressions are derived for the short term frequency stability, the power spectral density, and the power spectrum of the signal, as well as for the spectral density of the signal phase. The key to the results reported is an apparently novel perturbation technique which does not require smoothing of the instantaneous nonlinearity in the basic differential equation. Discussion of the solutions shows that the instantneous nonlinearities cause the device to act simultaneously like a linear AGC oscillator and like a high Q passive tuned circuit, with each aspect accorded one half the total noise excitation. Possible implications of this effect for other types of transient conditions in oscillators are indicated briefly.     

基于初值敏感性混沌弱信号检测的性能研究

混沌微弱信号检测研究是当前在时域检测信号的新方法,该文通过仿真计算研究了Duffing振子检测弱正弦信号的性能.首先分析了Duffing方程的运动状态,指出利用初值敏感性进行混沌弱信号检测时背景噪声必然对过渡过程产生影响;为分析噪声的影响,研究推导了连续系统仿真输入噪声的生成表达;仿真试验结果表明系统的最终运动状态跟随噪声强度的变化,由试验结果分析得出Duffing弱信号检测的最低信噪比,为混沌弱信号的实际应用提供指导.     

Synchronization-based trajectory generation method for a robotic suit using neural oscillators for hip joint support in walking

A new synchronization-based trajectory generation method is proposed for a robotic suit that is designed to assist walking by supporting the hip joints. Neural oscillators are connected to each hip joint of the robotic suit to synchronize the suit’s movement with the human user’s movement (outer synchronization). At the same time, mutual inhibition is incorporated between the neural oscillators on the left and right hip joints of the suit to help maintain a human-gait-like anti-phase relationship (inner inhibition). We developed a two-degree-of-freedom robotic suit, which consists of two actuators located where the human hip joints are. Each actuator has a built-in torque sensor, which measures the mutual joint torque generated if any difference arises between the movement of a user and that of the suit. The mutual joint torque serves as input to neural oscillators, which control each actuator of the robotic suit through outer synchronization with the mutual joint torque and inner inhibition. The inhibitory weight, used to adjust the inhibitory strength between neural oscillators, is designed in a series of simulations. We conducted walking experiments to show the validity of our proposal for walking assistance of the robotic suit with mutual inhibition between neural oscillators.     

Portable system for quantifying motor abnormalities in Parkinson'sdisease

To quantify motor disabilities in Parkinson's disease (PD), a compact, portable neurophysiological system based on a personal computer was designed to measure tremor, bradykinesia, and muscle tone. Tremor was detected by solid-state accelerometers and translated into a digital signal. The system displayed the root-mean-square (RMS) displacements and frequency distribution of the tremor in the horizontal and vertical planes, along with a reconstructed graphic image of the displacement. Bradykinesia was measured using a panel that detects release and depression of switches in response to auditory and visual signals; the system calculated subjects' reaction times and movement times in milliseconds. Tone at the elbow was measured by strapping the upper extremity to a lightweight low-friction cradle and then passively moving the cradle with an instrumented handle. Signals representing torque and arm angle were processed by the computer and displayed in real time on the screen with stiffness as a mean slope in Nm/degree. Clinical tests indicate that this system provides a convenient and accurate method for quantifying important aspects of the Parkinsonian syndrome and might be applicable to other movement disorders     

A new method of rotor resistance estimation for vector-controlledinduction machines

The estimation of rotor time constant, or rotor resistance, in a vector-controlled induction machine is necessary to achieve high-performance torque control. A new method of estimating the rotor resistance online, for use in a vector-controlled induction machine, is presented. It uses short duration pulses added to the constant flux reference current i_dse* and based on the resultant torque command current produced by a proportional-integral controller i_qse * adjusts the rotor resistance estimate. This method of self-tuning the vector controller to the rotor time constant, when operating in a closed-loop speed control loop, does not produce torque pulsations when correctly tuned. In comparison to other online methods such as the extended Kalman filter and the extended Luenberger observer, this method does not require voltage sensors and is computationally simpler. The rotor resistance estimation technique is illustrated through simulation and practical implementation of a vector-controlled induction machine     

Subspace methods for identification of human ankle joint stiffness

Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, since they appear and change together. Therefore, the direct estimation of the intrinsic and reflex stiffnesses is difficult. In this paper, we present a new, two-step procedure to estimate the intrinsic and reflex components of ankle stiffness. In the first step, a discrete-time, subspace-based method is used to estimate a state-space model for overall stiffness from the measured overall torque and then predict the intrinsic and reflex torques. In the second step, continuous-time models for the intrinsic and reflex stiffnesses are estimated from the predicted intrinsic and reflex torques. Simulations and experimental results demonstrate that the algorithm estimates the intrinsic and reflex stiffnesses accurately. The new subspace-based algorithm has three advantages over previous algorithms: 1) It does not require iteration, and therefore, will always converge to an optimal solution; 2) it provides better estimates for data with high noise or short sample lengths; and 3) it provides much more accurate results for data acquired under the closed-loop conditions, that prevail when subjects interact with compliant loads.     

An Inductive Conductivity Meter for Monitoring the Salinity of Dialysis Water

An inductive conductivity meter is described, especially adapted as a salinity monitor for dialysis water. Salinity are given. The principal problems of the inductive conductivity meter result from the low conductivity of electrolytes. The weak coupling due to the electrolyte means that stray coupling must be reduced to a very low level. This has been accomplished by means of a heavy copper eddy-current shield, which reduces the unwanted coupling to a level corresponding to a conductivity increment of the order of 10-8 ?-1. cm-1. The effect of parasitic impedances in the receiver core are minimized by designing this core as a current transformer, rather than a voltage transformer. The operating frequency is 5600 Hz. The circuits are described, including the oscillator, current-sensitive preamplifier, phase-sensitive detector, dc amplifiers, and alarm circuits. The phase-sensitive detector uses ordinary planar transistors and does not require a square wave reference signal.     

基于并联Duffing振子的微弱QPSK信号解调

针对微弱QPSK信号传统解调方法误码率高且其存在相位模糊的缺点,提出了一种基于并联Duffing振子的解调方法.该方法利用混沌振子对待测信号相位的敏感性,先将微弱QPSK信号通过并联Duffing振子系统,系统的输出是交替出现的周期状态与混沌状态,再计算信号功率谱密度的熵加以区别,进而判决解调QPSK信号.仿真结果表明...     

Variable flux control of permanent magnet synchronous motor drivesfor constant torque operation

In this paper, a small signal model of permanent magnet synchronous machines is developed which includes both components of torque, i.e., magnet torque and reluctance torque. The effects of flux variations on the torque are analyzed by the use of the developed model. The off-line torque compensation method proposed for induction machines is then adapted to permanent magnet motor drives to achieve a constant torque, variable flux operation of the drives. A sensitivity analysis is performed to show that the off-line method is influenced considerably by machine parameter variations. Therefore the concept of forced compensation is introduced and an on-line torque compensation controller is proposed. Simulation results are presented to show the effectiveness of the proposed controller. An experimental vector controlled permanent magnet motor drive including the on-line torque compensation controller is implemented based on a TMS320C31 DSP to evaluate the method. The experimental results also confirm a desirable variable flux control of the motor drive under constant torque operation     

Antialiasing Oscillators in Subtractive Synthesis

In this article, oscillator algorithms for digital subtractive synthesis were reviewed. The algorithms were divided into three categories: bandlimited, quasi-bandlimited, and alias-reducing methods. In the first category, the most interesting methods are in practice those that utilize wavetable techniques. The second category consists of methods that low-pass-filter the underlying continuous-time signal prior to sampling. The optimization of the previously introduced BLIT and BLEP methods were considered as a filter design problem. A new technique called the PolyBLEP method was introduced as a variation of the BLEP method that does not require a table lookup but is based on a closed-form formula. In the PolyBLEP algorithm, an integrated polynomial interpolation function is used for acquiring samples to correct the transition region of the waveform. In the third category, the DPW oscillator algorithm generates an approximate sawtooth waveform that has reduced aliasing. This recently proposed method is probably the simplest useful technique for this purpose, because only the trivial sawtooth is simpler, but it is practically useless due to its heavy aliasing. An alternative decimation filter was proposed for the DPW2X algorithm to suppress aliasing well in the frequency region where human hearing is most sensitive     

Simultaneous and nonlinear identification of mechanical and reflexproperties of human elbow joint muscles

The naturally coexisting intrinsic mechanical and reflex properties of the human elbow joint were identified simultaneously using nonlinear, time-delay, continuous-time, and dynamic models. Angular random perturbations of small amplitude and low bandwidth were applied to the joint using a computer-controlled servomotor, while the subject maintained various levels of mean background muscle torque. Joint neuromuscular dynamics were identified from the measured elbow angle and torque. Stretch reflexes were modeled nonlinearly with both dynamic and static reflex gains. A continuous-time system identification method was developed to estimate parameters of the nonlinear models directly from sampled data while retaining realistic physical or physiological interpretations. Results from six subjects showed that dynamic stretch reflex gains, joint stiffness, and viscosity generally increased with mean background muscle torque; and that dynamic stretch reflex gain was higher during muscle stretch than that during muscle shortening. More importantly, the study provided realistic simultaneous estimates of the relative contributions of intrinsic mechanical and reflex actions to net joint torque. In particular, reflexively-mediated stiffness generated a significant portion of the total joint stiffness and the percentage varied systematically with background muscle torque     

A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems

When the mobile environment consists of light-weight devices, the energy consumption of location-based services (LBSs) and the limited bandwidth of the wireless network become important issues. Motivated by this, we propose new spatial query processing algorithms to support Mobile Continuous Nearest Neighbor Query (MCNNQ) in wireless broadcast environments. Our solution provides a general client–server architecture for answering MCNNQ on objects with unknown, and possibly variable, movement types. Our solution enables the application of spatio-temporal access methods specifically designed for a particular type, to arbitrary movements without any false misses. Our algorithm does not require any conventional spatial index for MCNNQ processing. It can be adapted to static or moving objects, and does not require additional knowledge (e.g., direction of moving objects) beyond the maximum speed and the location of each object. Extensive experiments demonstrate that our location-based data dissemination algorithm significantly outperforms index-based solutions.     

Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search

In many studies and applications that include direct human involvement-such as human-robot interaction, control of prosthetic arms, and human factor studies-hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors, which are often very expensive and require bulky frames. Multilayer perceptron artificial neural networks (MLPANN) have been used commonly in the literature to model the relationship between surface EMG signals and muscle or limb forces for different anatomies. This paper investigates the use of fast orthogonal search (FOS), a time-domain method for rapid nonlinear system identification, for elbow-induced wrist force estimation. It further compares the forces estimated using FOS with the forces estimated by MLPANN for the same human anatomy under an ensemble of operational conditions. In this paper, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs. A single degree-of-freedom robotic experimental testbed has been constructed and used for data collection, training and validation.     

Biometric identification using fingertip electrocardiogram signals

In this research work, we present a newly fingertip electrocardiogram (ECG) data acquisition device capable of recording the lead-1 ECG signal through the right- and left-hand thumb fingers. The proposed device is high-sensitive, dry-contact, portable, user-friendly, inexpensive, and does not require using conventional components which are cumbersome and irritating such as wet adhesive Ag/AgCl electrodes. One of the other advantages of this device is to make it possible to record and use the lead-1 ECG signal easily in any condition and anywhere incorporating with any platform to use for advanced applications such as biometric recognition and clinical diagnostics. Furthermore, we proposed a biometric identification method based on combining autocorrelation and discrete cosine transform-based features, cepstral features, and QRS beat information. The proposed method was evaluated on three fingertip ECG signal databases recorded by utilizing the proposed device. The experimental results demonstrate that the proposed biometric identification method achieves person recognition rate values of 100% (30 out of 30), 100\(\%\) (45 out of 45), and 98.33\(\%\) (59 out of 60) for 30, 45, and 60 subjects, respectively.     

基于PVDF的肘关节控制肌群运动信息采集

针对当前肘关节控制肌群运动信息采集不足的问题,该文提出了一种基于聚偏氟乙烯(PVDF)传感器的肘关节控制肌群运动信息采集方法。论文阐述了PVDF的传感原理,设计并测试得到了传感器悬臂梁的最优结构,进行了传感器阵列及信号调理电路的设计。以徒手及负重10kg哑铃臂屈伸为实际测试内容,结果表明,该方法经济、可靠、实用,能够有效采集和监测运动时肘关节控制肌群的运动信息。     

Phase modulation of a loop phase-locked grid oscillator array

In this letter, we describe a method for phase modulation of a loop phase-locked grid oscillator array and report results obtained in a test bed implementation of the method. The key to the scheme lies in introducing the phase-locked loop (PLL) in such a way that the modulating data stream is introduced in parallel with the loop rather than through it, thereby circumventing the bandwidth limitation of the PLL. The experiment was performed at 4.7 GHz with a phase-locked grid oscillator array. The grid oscillator was successfully modulated by a 1 MHz signal, which is ten times higher than the bandwidth of the phase-locked loop     

Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular diseases. This paper presents a new method for nonlinear feature extraction of ECG signals by combining principal component analysis (PCA) and kernel independent component analysis (KICA). The proposed method first uses PCA to decrease the dimensions of the ECG signal training set and then employs KICA to calculate the feature space for extracting the nonlinear features. Support vector machine (SVM) is utilized to determine the nonlinear features of the ECG signal classification. Genetic algorithm is also used to optimize the SVM parameters. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. Computer simulations reveal that the proposed method yields more satisfactory classification results on the MIT–BIH arrhythmia database, reaching an overall accuracy of 97.78 %.     

Job search website for illiterate users of Pakistan

Illiteracy is a major hurdle in socio-economic development of Pakistan. Research in the Information and Communication Technology (ICT) can help in tackling this issue by developing solutions tailored for illiterate population. The work presented within this paper proposes a job search website for illiterate population of KPK province of Pakistan. The designed website does not require any human assistance while searching a job. Moreover, the website uses culturally relevant graphical and audio content. The usability of the website is tested according to the ISO-9241-11 specification. Furthermore, we have studied the effect of the participants’ characteristics, i.e., their age, computer and mobile usage experience on the usability of the website. The results show that the participants found the website usable. Specifically, they could search suitable jobs with a minimal number of clicks and in less time as compared to the usual task completion rates reported throughout the literature. None of the participants’ characteristics were found to be affecting the usability of the website. These results suggest a promising potential of ICT solutions for providing services to illiterate population of Pakistan.