**2.1. Measurement system design**

Block diagram of our EMG measurement system is shown in Fig. 1. Surface EMG signals are measured with three electrodes placed on a forearm. The EMG signals are preprocessed and converted into integrated EMG (IEMG) signals through the EMG measurement instrument. An IEMG signal has been used as an index of a muscle activity level in exercise physiology (Milner-Brown & Stein). Both EMG and IEMG are introduced into a PC to evaluate the averaged IEMG (AIEMG) features. An estimation algorithm of finger gesture is installed in the PC. After determining criterions of muscle activity, the proposed system identifies motions of fingers.

Hand Sign Classification Employing Myoelectric Signals of Forearm 311

**Figure 2.** Measured muscles and electrodes placed on forearm

calculated in terms of the moving average of the IEMG magnitude as

If you choose N=1, the AIEMG is the same as the IEMG signal.

**3. Forearm EMG signals regarding finger motion** 

The AIEMG feature is a periodic average of EMG signals during a designated interval (Yoshikawa et al., 2007). It is extracted from the IEMG in the 100 ms frame, which is shifted for 12.5 ms (80 Hz). Hamming window functions are applied to the signals in each frame. Since the measured data is converted into digital quantity by the PC interface, the AIEMG is

1

−

0 <sup>1</sup> ( ) , *N*

*t AIEMG k IEMG t N*

where AIEMG(k), and IEMG(t) represent the AIEMG feature of the k-th averaging frame and the IEMG magnitude of the t-th sample within the frame, respectively. Number of

The AIEMG eliminates momentary noise such as a spike, because it is a kind of low-pass filters. The larger you take the sampling number, the smoother the AIEMG signal becomes.

Not only muscles of fingers but of forearms work when you use your fingers. First of all, we have investigated the relationship between finger motion and the forearm EMG signals. Although we have obtained fundamental responses with regard to each single finger motion, this paper focuses only on typical gestures of composite finger configurations.

=

( )

<sup>=</sup> , (1)

**2.2. Electromyogram features** 

samples in a frame is denoted by N.

**Figure 1.** EMG measurement system

Figure 2 illustrates the forearm muscles whose EMG signals are measured by the instrument, and the positions of three electrodes placed on the forearm. The center figure shows forearm cross section of anatomical muscle placement.

Extensor pollicis brevis monitored with the electrode 1 (channel 1) is involved in finger extension. Extensor digitorum monitored with the electrode 2 (channel 2) is also involved in finger extension. Flexor digitorum profundus monitored with the electrode 3 (channel 3) is involved in finger flexion.

The EMG signals are measured with bipolar surface electrodes consisting of two parallel silver bars. These signals are amplified and converted into IEMG signals with rectification smoothing (the cutoff frequency 2.4 Hz) by means of a differential amplifier (Universal-EMG, Oisaka development Ltd.). The EMG signals are sampled at 10 kHz through a 16-bit A/D converter (PCI-3176, Interface Co.) and taken in a data-collection computer (Core i7 2.8 GHz, Windows 7).

**Figure 2.** Measured muscles and electrodes placed on forearm

#### **2.2. Electromyogram features**

Computational Intelligence in Electromyography Analysis – 310 A Perspective on Current Applications and Future Challenges

Block diagram of our EMG measurement system is shown in Fig. 1. Surface EMG signals are measured with three electrodes placed on a forearm. The EMG signals are preprocessed and converted into integrated EMG (IEMG) signals through the EMG measurement instrument. An IEMG signal has been used as an index of a muscle activity level in exercise physiology (Milner-Brown & Stein). Both EMG and IEMG are introduced into a PC to evaluate the averaged IEMG (AIEMG) features. An estimation algorithm of finger gesture is installed in the PC. After determining criterions of muscle activity, the proposed system

> Average IEMG

Criterion of motion

(PCI-3176) AIEMG Features Feature extraction Motion identification

Figure 2 illustrates the forearm muscles whose EMG signals are measured by the instrument, and the positions of three electrodes placed on the forearm. The center figure

Extensor pollicis brevis monitored with the electrode 1 (channel 1) is involved in finger extension. Extensor digitorum monitored with the electrode 2 (channel 2) is also involved in finger extension. Flexor digitorum profundus monitored with the electrode 3 (channel 3) is

The EMG signals are measured with bipolar surface electrodes consisting of two parallel silver bars. These signals are amplified and converted into IEMG signals with rectification smoothing (the cutoff frequency 2.4 Hz) by means of a differential amplifier (Universal-EMG, Oisaka development Ltd.). The EMG signals are sampled at 10 kHz through a 16-bit A/D converter (PCI-3176, Interface Co.) and taken in a data-collection computer (Core i7 2.8

 CPU : Core i7 2.80GHz Memory : 4.00GB OS : Windows 7

**2. EMG measurement system** 

**2.1. Measurement system design** 

identifies motions of fingers.

Electrodes

EMGmeasurement instrument (Universal-EMG)

A/D converter

EMG signals IEMG signals

shows forearm cross section of anatomical muscle placement.

**Figure 1.** EMG measurement system

involved in finger flexion.

GHz, Windows 7).

The AIEMG feature is a periodic average of EMG signals during a designated interval (Yoshikawa et al., 2007). It is extracted from the IEMG in the 100 ms frame, which is shifted for 12.5 ms (80 Hz). Hamming window functions are applied to the signals in each frame. Since the measured data is converted into digital quantity by the PC interface, the AIEMG is calculated in terms of the moving average of the IEMG magnitude as

$$AIEMG(k) = \frac{1}{N} \sum\_{t=0}^{N-1} IEMG(t)\_{t'} \tag{1}$$

where AIEMG(k), and IEMG(t) represent the AIEMG feature of the k-th averaging frame and the IEMG magnitude of the t-th sample within the frame, respectively. Number of samples in a frame is denoted by N.

The AIEMG eliminates momentary noise such as a spike, because it is a kind of low-pass filters. The larger you take the sampling number, the smoother the AIEMG signal becomes. If you choose N=1, the AIEMG is the same as the IEMG signal.

### **3. Forearm EMG signals regarding finger motion**

Not only muscles of fingers but of forearms work when you use your fingers. First of all, we have investigated the relationship between finger motion and the forearm EMG signals. Although we have obtained fundamental responses with regard to each single finger motion, this paper focuses only on typical gestures of composite finger configurations.

"Rock-paper-scissors" is a hand game played by two or more people. Each player changes his hand into one of three basic hand-signs representing rock, paper, or scissors as shown in Fig. 3. Each of the hand signs beats one of the other two, and loses to the other in the game.

Our purpose in this paper is to distinguish the hand-signs by analyzing the forearm EMG signals.

rock scissors paper

Hand Sign Classification Employing Myoelectric Signals of Forearm 313

**Figure 4.** EMG signals regarding "rock"

**Figure 3.** Hand signs to be distinguished
