Karan Veer

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Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of(More)
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(More)
Dual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals.(More)
  • Karan Veer
  • Journal of medical engineering & technology
  • 2015
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(More)
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(More)
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