Te-Son Kuo

Trace bounds on the solution of the algebraic matrix Riccati and Lyapunov equation

Lower and upper bounds on the trace of the positive semidefinite solution of the algebraic matrix Riccati and Lyapunov equation are derived. The upper trace bound obtained in this note in many cases results in a tighter bound as compared to the Upper bound for the maximal eigenvalue proposed in [1] and [2].

A neuro-control system for the knee joint position control with quadriceps stimulation

A neuro-control system was designed to control the knee joint to move in accordance with the desired trajectory of movement through stimulation of quadriceps muscle. This control system consisted of a neural controller and a fixed parameter proportional-integral-derivative (PID) feedback controller, which was designated as a neuro-PID controller. A multilayer feedforward time-delay neural network was used and trained as an inverse model of the functional electrical stimulation (FES)-induced quadriceps-lower leg system for direct feedforward control. The training signals for neural network learning were obtained from experimentation using a low-pass filtered random sequence to reveal the plant characteristics. The Nguyen-Widrow method was used to initialize the neural connection weights. The conjugate gradient descent algorithm was then used to modify these connection weights so as to minimize the errors between the desired outputs and the network outputs. The knee joint angle was controlled with only small deviations along the desired trajectory with the aid of the neural controller. In addition, the PID feedback controller was utilized to compensate for the residual tracking errors caused by disturbances and modeling errors. This control strategy was evaluated on one able-bodied and one paraplegic subject. The neuro-PID controller showed promise as a position controller of knee joint angle with quadriceps stimulation.

The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition [movements classification]

A new technique for classifying patterns of movement via electromyographic (EMG) signals is presented. Two methods (conventional autoregressive (AR) coefficients and cepstral coefficients) for extracting features from EMG signals and three classification algorithms (Euclidean Distance Measure (EDM), Weighted Distance Measure (WDM), and Maximum Likelihood Method (MLM)) for discriminating signals representative of broad classes of movements are described and compared. These three classifiers are derived from Bayes classifier with some assumptions, the relationship among them is discussed. The conventional MLM is modified to avoid heavy matrix inversion. Six able-bodied subjects with two pairs of surface electrodes located on bilateral sternocleidomastoid and upper trapezius muscles were studied in the experiment. The EMG signals of 20 repetitions of 10 motions were analyzed for each subject. Experimental results showed that mean recognition rate of the cepstral coefficients was at least 5% superior to that of the AR coefficients. The improvement achieved by the cepstral method was statistically significant for all the three classifiers. Reasons for the superiority of cepstral features were investigated from the feature space and frequency domain, respectively. The cepstral coefficients owned better cluster separability in feature space and they emphasized the more informative part in the frequency domain. The discrimination rate of the MLM was the highest among three classifiers. Incorporation of the cepstral features with the MLM could reduce the misclassification rate by 10.6% when compared with the combination of AR coefficients and EDM. Proper choice of five of ten motions could further raise the recognition rate to more than 95%.

Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model

Because the relations between electromyographic signal (EMG) and anisometric joint torque remain unpredictable, the aim of this study was to determine the relations between the EMG activity and the isokinetic elbow joint torque via an artificial neural network (ANN) model. This 3-layer feed-forward network was constructed using an error back-propagation algorithm with an adaptive learning rate. The experimental validation was achieved by rectified, low-pass filtered EMG signals from the representative muscles, joint angle and joint angular velocity and measured torque. Learning with a limited set of examples allowed accurate prediction of isokinetic joint torque from novel EMG activities, joint position, joint angular velocity. Sensitivity analysis of the hidden node numbers during the learning and testing phases demonstrated that the choice of numbers of hidden node was not critical except at extreme values of those parameters. Model predictions were well correlated with the experimental data (the mean root-mean-square-difference and correlation coefficient gamma in learning were 0.0290 and 0.998, respectively, and in three different speed testings were 0.1413 and 0.900, respectively). These results suggested that an ANN model can represent the relations between EMG and joint torque/moment in human isokinetic movements. The effect of different adjacent electrode sites was also evaluated and showed the location of electrodes was very important to produce errors in the ANN model.

An effective and efficient compression algorithm for ECG signals with irregular periods

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface

The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications.

Robust stability for linear time-delay systems with linear parameter perturbations

Abstract A new robust state feedback controller design in the time domain is developed for parametrically perturbed linear systems with time delay. The properties of the matrix measure and comparison theorem are employed to investigate the robust stability conditions of systems with linear structured or unstructured perturbations. A method is proposed to design the robust state feedback controller to satisfy the robustness requirement. Examples are given to illustrate the proposed method.

Evaluation of parametric methods in EEG signal analysis

In this paper, a well designed database, considering statistical characteristics and including all types of Electroencephalogram (EEG) is built. 900 EEG segments, each with a short interval (1.024 sec) in this database are clustered into eight classes. Three tests of white noise for evaluating the efficiency of autoregressive (AR) and autoregressive-moving average (ARMA) models are proposed. The Akaike information criterion (AIC) is used for determining orders of AR and ARMA models. The AR model requires a higher model order (8.67 on the average) than the ARMA model (6.17 on the average). However, it is found that about 96% of the 900 segments can be efficiently represented by the AR model, and only about 78% of them can be efficiently represented by ARMA model. We therefore conclude that the AR model is preferred for estimating EEG signals.

Review of telemedicine projects in Taiwan

Taiwan is a heavily populated country, with a small land area and many mountains and isolated islands. Because medical resources are unequally distributed, high quality accessible medical care is a major problem in rural areas. Medical personnel are unwilling to practice in rural areas because of fear of isolation from peers and lack of continuing medical education (CME) in those areas. Telemedicine provides a timeless and spaceless measure for teleconsultation and education. The development of telemedicine in Taiwan began under the National Information Infrastructure (NII) Project. Distance education and teleconsultation were the first experimental projects during the initiation research stage. The cost and effectiveness of the hardware and network bandwidth were evaluated. In the promotion research stage, applications in different medical disciplines were tested to promote multipoint videoconference, electronic journals and VOD. Investigation of user satisfaction put on more emphasis on improving application functions. In 1998, a new Cyber Medical Center (CMC) international collaboration project was begun, integrating technologies of multimedia, networking, database management, and the World Wide Web. The aim of the CMC is to create a multimedia network system for the management of electronic patient records, teleconsultation, online continuing medical education, and information services on the web. A Taiwan mirror site of Virtual Hospital and two international telemedicine trials through Next Generation Internet (NGI) were done at the end of 1998. In the future, telemedicine systems in Taiwan are expected to combine the Internet and broadband CATV, ADSL, and DBS networking to connect clinics, hospitals, insurance organizations, and public health administrations; and, finally, to extend to every household.

ROBUST LINEAR QUADRATIC OPTIMAL CONTROL FOR SYSTEMS WITH LINEAR UNCERTAINTIES

Abstract This paper presents two simple but effective algorithms for selecting the weighting matrices needed in designing linear quadratic optimal control for systems with linear uncertainties. By utilizing the Lyapunov stability criterion, it is shown that the optimal state feedback control law designed for the nominal system can stabilize the uncertain system, provided the uncertainties satisfy the so-called matching conditions and are within a given bounding set. The methods are tested for three examples, and the results show that the current methods have wider application ranges than some approaches treating similar problems in the literature. Furthermore, this paper considers the case in which matching conditions are not exactly satisfied. A measure of mismatch is adopted from the literature, and a threshold on the mismatch is found to guarantee that the optimal controller designed for the system without the mismatching uncertainties is effective when the mismatching part is added. Finally, a method ...