Abstract—People with transradial hand amputations can have control capabilities of prosthetic hands via surface electromyography (sEMG) but the control systems are limited and usually not natural. In the scientific literature, the application of pattern recognition techniques to classify hand movements in sEMG led to remarkable results but the evaluations are usually far from real life applications with all uncertainties and noise. Therefore, there is a need to improve the movement classification accuracy in real settings. Smoothing the signal with a low pass filter is a common pre-processing procedure to remove high-frequency noise. However, the filtering frequency modifies the signal strongly and can therefore affect the classification results. In this paper we analyze the dependence of the classification accuracy on the pre-processing low-pass filtering frequency in 3 hand amputated subjects performing 50 different movements. The results highlight two main interesting aspects. First, the filtering frequency strongly affects the classification accuracy, and choosing the right frequency between 1Hz-5Hz can improve the accuracy up to 5%. Second, different subjects obtain the best classification performance at different frequencies. Theoretically these facts could affect all the similar classification procedures reducing the classification uncertainity. Therefore, they contribute to set the field closer to real life applications, which could deeply change the life of hand amputated subjects.
 

Index Terms—surface electromyography, signal filtering, machine learning, rehabilitation engineering
 

Cite: Manfredo Atzori and Henning Müller, "Electromyography Low Pass Filtering Effects on the Classification of Hand Movements in Amputated Subjects," International Journal of Signal Processing Systems, Vol. 3, No. 2, pp. 118-122, December 2015. doi: 10.12720/ijsps.3.2.118-122