ABSTRACT
The human hand has multiple degrees of freedom (DOFs) to achieve high dexterity. Identifying the five-finger movements using surface electromyography (sEMG) is challenging. Moreover, the success rate of identifying the hand movements is sensitive to many aspects; for example, the sEMG electrode placements, variant movement forces, or movement speeds. In this chapter, a robust sEMG system for identifying the hand movements is developed. First, a multichannel sEMG sensor ring is designed, which is easy to wear on the human forearm even without knowledge of the exact location of the corresponding muscles. However, a new problem of using the multichannel sensor ring is followed and unsolved in the current research. The problem is how to relocate the sEMG electrodes with the same sequence as the last trial. This chapter introduces the concordance correlation coefficient to investigate the 220relationships of all channels, and autorelocation of the sEMG electrodes is possible. The process of the successful hand motion classification can be divided into collecting original sEMG signals, calculating features basd on original signals, and classifying motions based on features. If the calculated features are robust to some variances in the movement forces and speed, the motion classification results also have robustness. Thus, to make classification of the hand movements robust to some variances in the movement forces and speed, a new ratio measure of the multiple channels is defined as the feature, which is based on the results of the temporal square integral values of each channel signal. Finally, real-time classification of the hand movements is possible, using the statistical classifier based on Mahalanobis distance. In addition to classification of the hand movement types, knowing the movement force and the movement speed are important. In this chapter, the levels of the movement forces are described using spectral moments based on the short-time Fourier transform (STFT) results. The levels of the movement speeds, which are seldom studied in the current research, are also identified. Based on the results of STFT, the spectral flatness feature is firstly introduced for the sEMG signal to describe the different speeds of the hand movements.
