References
- Sharma, T., and Veer, K., 2014, Wavelet based feature extraction of electromyogram signal for denoising. International Journal of Advanced Research in Computer Engineering & Technology, 3, 2623–2627
- Davidson, J., 2005, A comparison of upper limb amputees and patients with upper limb injuries using the disability of the arm, shoulder and hand (Dash). Proceedings of Myoelectric Controls/Powered Prosthetics Symposium, New Brunswick, Canada
- Veer, K., 2014, Interpretation of surface electromyograms to characterize arm movement. Instrumentation Science & Technology, 42, 513–521
- Anand, S., 1994, Technology of sensors for clinical practice. IETE Technical Review, 11, 43–48
- Veer, K., and Agarwal, R., 2014, Wavelet denoising and evaluation of electromyogram signal using statistical algorithm. International Journal of Biomedical Engineering and Technology, 16, 293–305
- Tang, Y.-W., Lin, Y.-D., Lin, Y.-T., and Chakhap, J., 2013, Using conductive fabric for capacitive EEG measurements. IETE Technical Review, 30, 295–302
- Kumar, S., Chatterjee, A., and Kumar, A., 2012, Design of a below elbow myoelectric arm with proportional grip force. Journal of Scientific & Industrial Research, 71, 262–265
- Veer, K., Agarwal, R., and Kumar, A., 2014, Processing and interpretation of surface electromyogram signal to design prosthetic device. Robotica, DOI: 10.1017/S0263574714002409
- Phinyomark, A., Phukpattaranont, P., and Limsakul, C., 2011, A review of control methods for electric power wheelchairs based on electromyography signals with special emphasis on pattern recognition. IETE Technical Review, 28, 316–326
- Veer, K., 2015, Experimental study and characterization of SEMG signals for upper limbs. Fluctuation and Noise Letters, DOI: 10.1142/S0219477515500285
- Veer, K., and Agarwal, R., 2015, Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals. Journal of Applied Statistics, 42, 1591–1601
- Veer, K., 2015, A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier. Measurement, 60, 283–291