**3.1. EMG and IEMG signals**

We have evaluated the forearm EMG signals with our measurement system. When displaying "rock" by clenching fist, the EMG and IEMG signals were measured as shown in Figs. 4 and 5. Figure 4 (a1), (a2), and (a3) indicate the EMG signals measured with channels 1, 2, and 3, respectively, where the horizontal axis expresses time, and the vertical represents magnitude of the EMG signal. The IEMG signals are also evaluated as shown in Fig. 5. Figure 5 (b1), (b2), and (b3) indicate the IEMG signals measured with channels 1, 2, and 3, respectively, where the horizontal axis expresses time and the vertical represents magnitude of the IEMG signal. Those waveforms shown in Figs. 4 and 5 indicate that channel 1 is inactive, channel 2 is less active, and only channel 3 is active. It can be considered that flexor digitorum profundus is mainly working when you shape "rock" with your hand.

 "Scissors" are represented by two fingers extended and separated. The EMG and IEMG signals were measured regarding "scissors" as shown in Figs. 6 and 7, respectively. These figures show that channel 2 is solely active and channels 1 and 3 are almost inactive. Results support that extensor digitorum contributes to showing "scissors."

An open hand signifies "paper." The EMG and IEMG signals are shown with regard to "paper" in Figs. 8 and 9, respectively. They indicate both channels 1 and 2 are active and channel 3 is less active. It is surmised that "paper" is formed owing to both extensor pollicis brevis and extensor digitorum.

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

Computational Intelligence in Electromyography Analysis – 312 A Perspective on Current Applications and Future Challenges

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

**3.1. EMG and IEMG signals** 

with your hand.

brevis and extensor digitorum.

game.

signals.

"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

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

We have evaluated the forearm EMG signals with our measurement system. When displaying "rock" by clenching fist, the EMG and IEMG signals were measured as shown in Figs. 4 and 5. Figure 4 (a1), (a2), and (a3) indicate the EMG signals measured with channels 1, 2, and 3, respectively, where the horizontal axis expresses time, and the vertical represents magnitude of the EMG signal. The IEMG signals are also evaluated as shown in Fig. 5. Figure 5 (b1), (b2), and (b3) indicate the IEMG signals measured with channels 1, 2, and 3, respectively, where the horizontal axis expresses time and the vertical represents magnitude of the IEMG signal. Those waveforms shown in Figs. 4 and 5 indicate that channel 1 is inactive, channel 2 is less active, and only channel 3 is active. It can be considered that flexor digitorum profundus is mainly working when you shape "rock"

rock scissors paper

 "Scissors" are represented by two fingers extended and separated. The EMG and IEMG signals were measured regarding "scissors" as shown in Figs. 6 and 7, respectively. These figures show that channel 2 is solely active and channels 1 and 3 are almost inactive. Results

An open hand signifies "paper." The EMG and IEMG signals are shown with regard to "paper" in Figs. 8 and 9, respectively. They indicate both channels 1 and 2 are active and channel 3 is less active. It is surmised that "paper" is formed owing to both extensor pollicis

support that extensor digitorum contributes to showing "scissors."

Computational Intelligence in Electromyography Analysis – 314 A Perspective on Current Applications and Future Challenges

Hand Sign Classification Employing Myoelectric Signals of Forearm 315

**Figure 6.** EMG signals regarding "scissors"

**Figure 5.** IEMG signals regarding "rock"

**Figure 6.** EMG signals regarding "scissors"

Computational Intelligence in Electromyography Analysis – 314 A Perspective on Current Applications and Future Challenges

**Figure 5.** IEMG signals regarding "rock"

Computational Intelligence in Electromyography Analysis – 316 A Perspective on Current Applications and Future Challenges

Hand Sign Classification Employing Myoelectric Signals of Forearm 317

**Figure 8.** EMG signals regarding "paper"

**Figure 7.** IEMG signals regarding "scissors"

**Figure 8.** EMG signals regarding "paper"

Computational Intelligence in Electromyography Analysis – 316 A Perspective on Current Applications and Future Challenges

**Figure 7.** IEMG signals regarding "scissors"

Computational Intelligence in Electromyography Analysis – 318 A Perspective on Current Applications and Future Challenges

Hand Sign Classification Employing Myoelectric Signals of Forearm 319

(1)

**Figure 9.** IEMG signals regarding "paper"

Computational Intelligence in Electromyography Analysis – 318 A Perspective on Current Applications and Future Challenges

**Figure 9.** IEMG signals regarding "paper"

(1)

Hand Sign Classification Employing Myoelectric Signals of Forearm 321

(3)

(2)

Computational Intelligence in Electromyography Analysis – 320 A Perspective on Current Applications and Future Challenges

(2)

(3)

Computational Intelligence in Electromyography Analysis – 322 A Perspective on Current Applications and Future Challenges

Hand Sign Classification Employing Myoelectric Signals of Forearm 323

(4)

(N = 200)

**Figure 11.** AIEMG signals regarding "scissors"

**Figure 10.** AIEMG signals regarding "rock"

Computational Intelligence in Electromyography Analysis – 322 A Perspective on Current Applications and Future Challenges

(4)

**Figure 10.** AIEMG signals regarding "rock"

**Figure 11.** AIEMG signals regarding "scissors"

Computational Intelligence in Electromyography Analysis – 324 A Perspective on Current Applications and Future Challenges

Hand Sign Classification Employing Myoelectric Signals of Forearm 325

We next calculated the averaged IEMG (AIEMG) signals from IEMG according to eq. (1). They are stable and noiseless compared to the IEMG, because the feature prevents instantaneous noise of IEMG signals. It is important to adopt the optimum sampling number, N, to obtain the ideal AIEMG feature. When the number is too small, the signal intensity fluctuates and it is difficult to obtain consistent feature at every measurement. If it

Figure 10 shows the examples of AIEMG derived from IEMG signals indicated in Fig. 5, with regard to the number of sampling, N as 1, 50, 100, 200, 500, 1000, 2000, and 5000.

Figure 10(c1\_1), for instance, displays the AIEMG signal measured by channels 1 with sampling number of 1, where the horizontal axis expresses time, and the vertical represents

Figures 11 and 12 are representations of the AIEMG signals regarding channels 2 and 3,

When evaluating the experimental results, we have Table 1 which indicates contribution of muscles to gesticulation by hands. Extensor pollicis brevis does its part only in displaying "paper" among three signs. Extensor digitorum works when forming "scissors" and

This table helps us to classify displayed finger signs based only on the forearm surface EMG signals in real time. If obtaining any of the specific EMG signal combinations shown in the

Note that an electrode does not necessarily catch signals only when the corresponding muscle works. Thus, it is necessary to differentiate active signals from inactive to identify

Muscle Rock Scissors Paper Ch.1 Extensor pollicis brevis × × ○ Ch.2 Extensor digitorum × ○ ○ Ch.3 Flexor digitorum profundus ○ × ×

We have next investigated an identification of finger signs by analyzing the AIEMG of a forearm. The EMG signals were detected by three electrodes put on the forearm skin of the subjects, and active signals were distinguished from inactive ones according to the following

is too large, the AIEMG signal becomes blunt and thus its original waveform is lost.

**4. Finger sign identification based on forearm AIEMG signals** 

"paper." Flexor digitorum profundus contributes only to indication of "rock."

**3.2. AIEMG signals** 

magnitude of the AIEMG signal.

muscle motion precisely.

**Table 1.** Muscle activity pattern for finger sign

**4.2. Criterion of muscle activity**

○: Active ×: Inactive

respectively, whose sampling number is 200.

**4.1. Muscle activity corresponding to finger sign** 

table, we can deduce one of the finger signs among three.

**Figure 12.** AIEMG signals regarding "paper"
