Karan Veer

Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals

In this investigation, extracted features ofsignals have been analyzed for the recognition of arm movements. Short-time Fourier transform and wavelet transform based on Euclidian distance were applied to reordered signals. Results show that wavelet is a more useful and powerful tool for analyzing signals, since it shows multiresolution property with a significant reduction in the computation time for eliminating resolution problems. Finally, a statistical technique of repeated factorial analysis of variance for experimental recorded data was implemented in a way to investigate the effect of class separability for multiple motions for establishing surface electromyogram–muscular force relationship.

INTERPRETATION OF SURFACE ELECTROMYOGRAMS TO CHARACTERIZE ARM MOVEMENT

In this investigation, surface electromyogram signals were studied from muscles above the elbow to characterize operations of the arm. The instrumentation consisted of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. A program code was written and used to acquire the signal from the hardware, and interpretation was done to estimate parameters using a simulated algorithm. Different types of arm operations were analyzed using one way repeated factorial analysis of variance to justify the use of the surface electromyogram signal to identify arm motions. The results also present operations that may be easily realized by prosthetic devices. This work provides fundamental information to develop more powerful, flexible, and efficient prosthetic design.

Wavelet denoising and evaluation of electromyogram signal using statistical algorithm

In this study, wavelet analysis has been exercised to understand the quality of surface electromyogram signal for class separability. The surface electromyogram signals were estimated with the following steps. First, the obtained signal was decomposed using wavelet transform. The decomposed coefficients were then analysed with threshold methods. With the appropriate choice of wavelet, it is possible to remove interference noise effectively in order to analyse the signal. This paper presents a comparative study of different Daubechies wavelets (db2-db14) family for analysis of arm motions. From the analysed results, it was inferred that wavelet db4 performs denoising the best among the wavelets and is suitable for accurate classification of surface electromyogram signal. Further, one-way repeated factorial Analysis of Variance (ANOVA) statistical technique was also implemented to investigate the voluntary muscular contraction relationship for different arm movements.

Processing and interpretation of surface electromyogram signal to design prosthetic device

SUMMARY The study of arm muscles for independent operations leading to prosthetic design was carried out. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for different arm motions and then repeated factorial analysis of variance (ANOVA) technique was implemented to analyze effectiveness of signal. The electronic design consisted of analog and digital signal processing and controlling circuit and mechanical assembly consisted of wrist, palm and the fingers to grip the object in addition to a screw arrangement connected to a low power DC motor and gear assembly to open or close the hand. The wrist is mechanically rotated to orient the hand in a direction suitable to pick up/hold the object. The entire set up is placed in a casing which provides a cosmetic appeal to the artificial hand and the connected arm. The design criteria include electronic control, reliability, light weight, variable grip force with ease of attachment for simple operations like opening, grasping and lifting objects of different weight with grip force slightly more than enough just like that of a natural hand.

A novel feature extraction for robust EMG pattern recognition

Abstract This paper presents the detailed evaluation and classification of Surface Electromyogram (SEMG) signals at different upper arm muscles for different operations. After acquiring the data from selected locations, interpretation of signals was done for the estimation of parameters using simulated algorithm. First, different types of arm operations were analysed; then statistical techniques were implemented for investigating muscle force relationships in terms of amplitude estimation. The classification (Artificial Neural Network) based results have been presented for detecting different pre-defined arm motions in order to discriminate SEMG signals. The outcome of research indicates that a neural network classifier performs best with an average classification rate of 92.50%. Finally, the result also inferred the operations which were observed to be easy for arm recognition and the study is a step forward to develop powerful, flexible and efficient prosthetic designs.

EMG classification using wavelet functions to determine muscle contraction

Abstract Surface electromyogram (SEMG) is a complex signal and is influenced by several external factors/artifacts. The electromyogram signal from the stump of the subject is picked up through surface electrodes. It is amplified and artifacts are removed before digitising it in a controlled manner so that minimum signal loss occurs due to processing. As removing these artifacts is not easy, feature extraction to obtain useful information hidden inside the signal becomes a different process. This paper presents methods of analysing SEMG signals using discrete wavelet Transform (DWT) for extracting accurate patterns of the signals and the performance of the used algorithms is being analysed rigorously. The obtained results suggest a root mean square difference (RMSD) value for the denoising and quality of reconstruction of the SEMG signal. The result shows that the best mother wavelets for tolerance of noise are second order of symmlets and bior6.8. Results inferred that bior6.8 suitable for the classification and analysis of SEMG signals of different arm motions results in a classification accuracy of 88.90%.

A neural network-based electromyography motion classifier for upper limb activities

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition ...

Comparative study of FIR and IIR filters for the removal of 50 Hz noise from EEG signal

Small amplitude (μV) of the Electroencephalography (EEG) signal is contaminated by various artefacts in a recorded signal and changes the originality of the signal. The most common disturbance among them is power-line frequency noise of 50 Hz. This makes clinical analysis and information retrieval difficult. It is necessary to remove all such disturbances in EEG signals for proper diagnosis. In this study, performance analysis of Finite Impulse Response (FIR) filter based on various windows and Infinite Impulse Response (IIR) filters for noise reduction from EEG signals have been done. Digital FIR and IIR filter of 100th order applied to signal epochs were studied and performance analysis was done by calculating the fast Fourier transform and signal-to-noise ratio. The result shows that Kaiser window-based FIR filters is better at removing power-line noise from EEG signal.

An analytical approach to test and design upper limb prosthesis

Abstract In this work the signal acquiring technique, the analysis models and the design protocols of the prosthesis are discussed. The different methods to estimate the motion intended by the amputee from surface electromyogram (SEMG) signals based on time and frequency domain parameters are presented. The experiment proposed that the used techniques can help significantly in discriminating the amputee’s motions among four independent activities using dual channel set-up. Further, based on experimental results, the design and working of an artificial arm have been covered under two constituents—the electronics design and the mechanical assembly. Finally, the developed hand prosthesis allows the amputated persons to perform daily routine activities easily.

Identification and classification of upper limb motions using PCA

Abstract: This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.