M. B. I. Reaz

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.

Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

Design and Simulation of RF-CMOS Spiral Inductors for ISM Band RFID Reader Circuits

The recent popularity of RFID tags has generated research for accompanying miniature, low-power reader circuits. This work illustrates the design of RF complementary metal- oxide-semiconductor (CMOS) process compatible spiral inductors. Several simulators such as AWR Microwave Officereg, SONNETreg, and finite element program CST were used to provide its S21 and S31 transmission characteristics, approximate and finalized design layout values respectively. This design utilized Silterra 0.18 mum RF-CMOS technology process parameters. Simulation results indicate that inductors core diameters must be adequately large (more than 100 mum) to ensure high quality factor characteristics and its conductor spacing should be minimal to obtain larger per unit area inductive value. The proposed design methodology optimizes the conductor width of inductors to allow alignment of the peak quality factor with the circuits operating frequency, thereby enhancing the input/output matching characteristics and S-parameter extraction in the GHz region.

FPGA realization of power quality disturbance detection: an approach with wavelet, ANN and fuzzy logic

Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis. This paper presents new approach aimed at automating the analysis of power quality disturbances including sag, swell, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. The system is modelled using VHDL followed by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%.