Signal processing for the multistate myoelectric channel

In the multistate myoelectric channel, a single myoelectric signal source is used to control a multifunction powered prosthesis. The selection of a prosthesis function requires a receiver to process the myoelectric signal, contaminated with noise, and to decide on the basis of the received signals which function is desired. Thus the channnel cleady presents a problem of choice of receiver and of decision strategy. Previous sotutions to this problem have been basically empirical. In this paper we seek the optimum receiver where optimum is in the minimum probability of error sense. First a model is developed for the bipolar myoelectric signal to provide information about the relevant signal parameters and statistics. Using this information the Bayes minimum probability of error receiver is derived for an orbitrary signal parameter set. The optimum signal parameter set is then found for the Bayes receiver, and the receiver performance calculated. The receiver performance is measured and compared with the calculated performance. A significant performance improvement is seen in the optimum receiver over a more conventional receiver.     

Comments on "Signal processing for the multistate myoelectric channel"

This communication is a reply to statements made by Parker et al. regarding work by Graupe et al. on myoelectric multistate signal processing. In particular, it aims to correct the misconception in [1] that the work by Graupe et al. is based on using several electrode sites and that no attempt was made at evaluating performance, noting that the single electrode-site aspect was, and is, fundamental in that work.     

Spatial filtering for robust myoelectric control

Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods.     

A new strategy for multifunction myoelectric control

A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach     

A robust,real-time control scheme for multifunction myoelectric control

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.     

A wavelet-based continuous classification scheme for multifunction myoelectric control

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.     

Signal processing for networked multimedia

Real-time transmission of multimedia data over packet networks poses several interesting problems for signal processing research. Although the range of these problems covers a large variety of topics, currently two groups appear to attract the most attention. The first group concerns adapting the signal compression techniques to address the special requirements imposed by the packet networks, including accommodating for packet losses, delays, and jitter; providing capability for multipoint; and coping with the heterogeneous nature of today's networks. The second group of problems is related to protecting the intellectual property rights associated with the transmitted multimedia data. The increasing availability of high-bandwidth networking makes it extremely easy to illegally duplicate and disseminate digital information. Unless a mechanism can be established to protect the rights of the content providers, commercial use of networked multimedia will remain extremely limited     

Signal processing for optical communication

Optical communication plays a significant and increasing role in our society. The public demand for higher network speed requires an optical backbone network with larger capacity. Accompanying high transmission-rate optical communications system are severe technical specifications for optical devices and systems. Many popular optical devices could be represented with a digital filter model as described in this article. Use of well-developed signal processing techniques and algorithms to design these optical devices is a wise use of existing technology. The wavelength division multiplexing (WDM) system, which is the dominating optical communication system, is introduced in this article. Three signal processing application examples for optical communications are presented: optical wavelength interleaver, an all-pass filter for chromatic dispersion compensation, and an electronic equalizer. As demonstrated in this article, signal processing could play an important role in the development of advanced optical communication systems. However, as demonstrated in the case of an electronic equalizer, some optical system characteristics may require special attention if signal processing techniques are to be applied successfully. Therefore, interdisciplinary cooperation between researchers in optics and signal processing will be crucial for optical communications to fully benefit from signal processing.     

多通道表面肌电信号同步采样与处理研究

在康复机器人的人机交互系统中,表面肌电信号(sEMG)发挥着重要作用。针对采集单通道的肌电信号已经不能满足获取更多信息量的需求,该文设计一个8通道的肌电信号同步采集系统。该系统包括表面电极贴片、仪表放大器、带通滤波与二级放大、50 Hz陷波器、同步采集ADC和无线蓝牙模块等部分。通过测试实验,该采集系统很好地采集了人体的表面肌电信号,有效去除了共模噪声和50 Hz的工频干扰。获取的同步肌电信号可以进一步用于人机交互系统的模式识别研究。     

Adaptive cancellation technique in processing myoelectric activityof respiratory muscles

Spectral analysis of myoelectric activity of the chest muscles is a promising diagnostic tool for respiratory diseases. Reduction of the corrupting cardiac activity from the recorded myoelectric activity of the chest muscles is essential because of the spectral overlap of both the signals. The adaptive noise cancellation technique was used to reduce the interfering cardiac activity from the recorded myoelectric activity. The adaptive noise canceller implemented in a transversal structure was found to successfully reduce the corrupting cardiac activity. This paper describes the cancellation of the corrupting cardiac activity from the recorded myoelectric activity using adaptive noise cancellation technique and the characteristic features of spectra of EMG signals.     

Continuous myoelectric control for powered prostheses using hidden Markov models

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.     

Signal processing for underwater acoustic communications

The performance and complexity of signal processing systems for underwater acoustic communications has dramatically increased over the last two decades. With its origins in noncoherent modulation and detection for communication at rates under 100 b/s, phase-coherent digital communication systems employing multichannel adaptive equalization with explicit symbol-timing and phase tracking are being deployed in commercial and military systems, enabling rates in excess of 10 kb/s. Research systems have been shown to further dramatically increase performance through the use of spatial multiplexing. Iterative equalization and decoding has also proven to be an enabling technology for dramatically enhancing the robustness of such systems. This article provides a brief overview of signal processing methods and advances in underwater acoustic communications, discussing both singlecarrier and emerging multicarrier methods, along with iterative decoding and spatial multiplexing methods.     

Signal processing for hyperspectral image exploitation

Electro-optical remote sensing involves the acquisition of information about an object or scene without coming into physical contact with it. This is achieved by exploiting the fact that the materials comprising the various objects in a scene reflect, absorb, and emit electromagnetic radiation in ways characteristic of their molecular composition and shape. If the radiation arriving at the sensor is measured at each wavelength over a sufficiently broad spectral band, the resulting spectral signature, or simply spectrum, can be used (in principle) to uniquely characterize and identify any given material. An important function of hyperspectral signal processing is to eliminate the redundancy in the spectral and spatial sample data while preserving the high-quality features needed for detection, discrimination, and classification. This dimensionality reduction is implemented in a scene-dependent (adaptive) manner and may be implemented as a distinct step in the processing or as an integral part of the overall algorithm. The most widely used algorithm for dimensionality reduction is principal component analysis (PCA) or, equivalently, Karhunen-Loeve transformation     

Signal processing forum

Asymptotic analyses are abundant in the literature. However, some people (practitioners or not) question their usefulness. Others argue that they are useful. We do the same here. More exactly, we try to explain briefly why “asymptomania” is not a “dangerous disorder”, but rather an addiction to a useful tool. One can readily identify the following possible goals of an asymptotic (in N or SNR, etc.) analysis [where N is the number of available data samples, and SNR is the signal-to-noise ratio]: (a) derivation (or estimation) of the theoretical performance of a parameter estimator or detector; (b) analytical comparisons of the detection or estimation performance of a number of methods; and (c) the optimal choice of the user's variables of a detection or estimation method     

Selective classification for improved robustness of myoelectric control under nonideal conditions

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.     

Signal processing for future home-use digital VTR's

The author considers, mainly from the viewpoint of signal processing technologies, the current efforts for future home-use digital videotape recorders (VTRs). Recording technologies for future home-use digital VTRs are reviewed with respect to both current TV and HDTV systems. High-density recording and bit rate reduction are the major technologies needed for digital video recording with small cassettes. Recent experiments show that the technical problems, except those of equipment size and cost, have almost been solved     

Signal processing and standardization

Because in many instances signal processing is geared to communication, the attitude of the signal processing community regarding standardisation has always been very positive, leading to successful exploitation of its findings in the marketplace. Such standards as A-law/μ-law PCM, Group 3 facsimile, the Compact Disc, G.721, H.261, JPEG, MPEG-1, and MPEG-2 are all fine examples of how the signal processing technology available at a certain point in time was exploited to make successful signal processing standards. The fact that different standards were defined by different is just proof of the strategic value of a standard to favour or limit communications     

Support vector machine-based classification scheme for myoelectric control applied to upper limb

This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.