220
Biologically Inspired Robotics
relationships 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.
12.1 Introduction
The surface electromyography (sEMG) signal is generated by the electrical
activity of muscle fibers during a contraction and is noninvasively recorded
by electrodes attached to the skin (Merletti and Parker 2004). Its application
to control of artificial limbs or duplication of human movements using a
remote mechanism is challenging. In the rehabilitation field, sEMG signals
have been applied to control prosthetic legs (Jin et al. 2000) and prosthetic
arms (Doringer and Hogan 1995; Ito et al. 1992; Saridis and Gootee 1982).
Identification of human hand movements is relatively difficult, because the
hand possesses more degrees of freedom (DOFs) than the legs and arms. Due
to identification difficulties, the dexterity of some sEMG prosthetic hands in
the market is far less than that of the human hand, achieving only a limited
number of movements; that is, hand open and hand close. Many researchers
focus on dexterity improvement of sEMG prosthetic hands (Farry, Walker,
and Barabiuk 1996; Fukuda, Tsuji, and Kaneko 1997; Hudgins and Parker
1993; Kuribayashi, Okimura, and Taniguchi 1993), and discrimination of two
to six patterns can be achieved. In this chapter the aim is to further increase
the number of identified hand movements, and classification of seven hand
movements is achieved.
The placement of sEMG electrodes is a critical issue for successful identification of hand movements. Most of the current research is based on the idea
Biologically Inspired Robotics
relationships 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.
12.1 Introduction
The surface electromyography (sEMG) signal is generated by the electrical
activity of muscle fibers during a contraction and is noninvasively recorded
by electrodes attached to the skin (Merletti and Parker 2004). Its application
to control of artificial limbs or duplication of human movements using a
remote mechanism is challenging. In the rehabilitation field, sEMG signals
have been applied to control prosthetic legs (Jin et al. 2000) and prosthetic
arms (Doringer and Hogan 1995; Ito et al. 1992; Saridis and Gootee 1982).
Identification of human hand movements is relatively difficult, because the
hand possesses more degrees of freedom (DOFs) than the legs and arms. Due
to identification difficulties, the dexterity of some sEMG prosthetic hands in
the market is far less than that of the human hand, achieving only a limited
number of movements; that is, hand open and hand close. Many researchers
focus on dexterity improvement of sEMG prosthetic hands (Farry, Walker,
and Barabiuk 1996; Fukuda, Tsuji, and Kaneko 1997; Hudgins and Parker
1993; Kuribayashi, Okimura, and Taniguchi 1993), and discrimination of two
to six patterns can be achieved. In this chapter the aim is to further increase
the number of identified hand movements, and classification of seven hand
movements is achieved.
The placement of sEMG electrodes is a critical issue for successful identification of hand movements. Most of the current research is based on the idea