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Classification of Hand Motion Using Surface EMG Signals
that the distribution of the corresponding muscles for hand movements is
known. The classification success rates are dominantly determined by the
placement of the sEMG electrodes. However, in many applications, for example, commercial products, most users lack knowledge of the muscle distribution, and thus there is a risk of classification failure due to misalignment of
the sEMG electrodes. To solve this problem, in recent research, multichannel
sensor rings have been designed (Y.-C. Du et al. 2010; Saponas et al. 2008).
Since multichannel sensor rings envelop the whole or half circumference of
the forearm, sensor rings can capture all signals from the extensor or flexor
muscles of the forearm. In this chapter, the sEMG sensor is designed as a
half wristband, and the user can easily wear the sensor ring on the wrist as
if wearing a watch.
The features extracted from the raw sEMG signals are another critical
issue for successful identification. For research in which sEMG electrodes
are pasted exactly above the corresponding muscles, methods include the
temporal features (Zecca et al. 2002) for noncomplex and low-speed movements and temporal–spectral features; for example, short-time Fourier transform (STFT) and short-time Thompson transform (STTT), which can provide
more transient information for complex and high-speed movements (S. J.
Du and Vuskovic 2004; Farry, Walker, and Barabiuk 1996; Hannaford and
Lehman 1986). For a multichannel sensor ring, the methods of feature extraction include the ratios of the temporal and spectral features among the different channels (Saponas et al. 2008) and six temporal features directly used
for the motion classifier (Y.-C. Du et al. 2010). This chapter further improves
the feature extraction method for the multichannel sensor ring by increasing classification robustness to some variances of 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.
For a multichannel sensor ring, a new problem has arisen, which is how
to recognize the same channel sequence as the last trial after the user arbitrarily wears the sensor ring. Currently, no studies have been conducted to
solve this problem. This chapter is inspired by research using the cross-correlation coefficient to investigate the cross-talk among the different channels
(Mogk and Keir 2003). The concordance correlation coefficient is introduced
to study the relationship between multiple channels and check the feasibility
of channel sequence recognition.
The speed of hand movements is also critical for movement description.
Currently, there are few studies concentrated on this topic. In this chapter,
the levels of the movement speeds are identified. Based on the results of
STFT, the spectral flatness feature is firstly introduced for the sEMG signal to
describe the different speeds of hand movements.
Classification of Hand Motion Using Surface EMG Signals
that the distribution of the corresponding muscles for hand movements is
known. The classification success rates are dominantly determined by the
placement of the sEMG electrodes. However, in many applications, for example, commercial products, most users lack knowledge of the muscle distribution, and thus there is a risk of classification failure due to misalignment of
the sEMG electrodes. To solve this problem, in recent research, multichannel
sensor rings have been designed (Y.-C. Du et al. 2010; Saponas et al. 2008).
Since multichannel sensor rings envelop the whole or half circumference of
the forearm, sensor rings can capture all signals from the extensor or flexor
muscles of the forearm. In this chapter, the sEMG sensor is designed as a
half wristband, and the user can easily wear the sensor ring on the wrist as
if wearing a watch.
The features extracted from the raw sEMG signals are another critical
issue for successful identification. For research in which sEMG electrodes
are pasted exactly above the corresponding muscles, methods include the
temporal features (Zecca et al. 2002) for noncomplex and low-speed movements and temporal–spectral features; for example, short-time Fourier transform (STFT) and short-time Thompson transform (STTT), which can provide
more transient information for complex and high-speed movements (S. J.
Du and Vuskovic 2004; Farry, Walker, and Barabiuk 1996; Hannaford and
Lehman 1986). For a multichannel sensor ring, the methods of feature extraction include the ratios of the temporal and spectral features among the different channels (Saponas et al. 2008) and six temporal features directly used
for the motion classifier (Y.-C. Du et al. 2010). This chapter further improves
the feature extraction method for the multichannel sensor ring by increasing classification robustness to some variances of 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.
For a multichannel sensor ring, a new problem has arisen, which is how
to recognize the same channel sequence as the last trial after the user arbitrarily wears the sensor ring. Currently, no studies have been conducted to
solve this problem. This chapter is inspired by research using the cross-correlation coefficient to investigate the cross-talk among the different channels
(Mogk and Keir 2003). The concordance correlation coefficient is introduced
to study the relationship between multiple channels and check the feasibility
of channel sequence recognition.
The speed of hand movements is also critical for movement description.
Currently, there are few studies concentrated on this topic. In this chapter,
the levels of the movement speeds are identified. Based on the results of
STFT, the spectral flatness feature is firstly introduced for the sEMG signal to
describe the different speeds of hand movements.