**5. Conclusion**

Computational Intelligence in Electromyography Analysis – 332 A Perspective on Current Applications and Future Challenges

**Table 3.** Accuracy rate of finger sign identification

**Figure 15.** Experimental results of finger sign identification

N Rock (%) Scissors (%) Paper (%) Total(%)

1 97.5 87.5 57.5 80.8

50 97.5 97.5 65.0 86. 7

100 95.0 95.0 70.0 86. 7

200 97.5 97.5 97.5 97.5

500 95.0 85.0 70.0 83.3

1000 90.0 77.5 82.5 83.3

2000 80.0 62.5 57.5 66. 7

5000 37.5 27.5 20.0 28.3

We have studied the estimation method of hand signs employing the electromyogram. This paper focused on the forearm EMG signals caused by the finger motion. It relys on the proposition that the specific muscles of forearms work even if you only move your fingers.

First of all, the EMG measurement system was designed to detect signals from the surface skin of forearms. We constructed three-channel myoelectric signal processing system by assigning three forearm muscles; extensor pollicis brevis, extensor digitorum, and flexor digitorum profundus. It provided EMG and IEMG signals, and also calculated AIEMG features.

Fundamental experiments were carried out next to acquire data regarding the relationship between the finger motion and the forearm EMG signals. Investigation on myoelectric responses revealed that the specified forearm muscles were activated with respect to the corresponding finger motion.

The disclosed principles were applied to identification of typical hand signs such as "rock," "paper," and "scissors" in terms of the well-known hand game. We obtained correlative experimental data of hand signs and the myoelectric signals.

We found out the activity pattern of forearm muscles with regard to each hand sign as follows. If shaping "rock," flexor digitorum profundus is mainly working. When "scissors" are indicated, extensor digitorum is activated. Display of "paper" is owing to both extensor pollicis brevis and extensor digitorum.

We established the following principles in consequence to deduce the hand sign from the activities of forearm muscles. Extensor pollicis brevis is active in displaying "paper." Extensor digitorum operates when forming "scissors" and "paper." Flexor digitorum profundus contributes only to indication of "rock."

We then designed the classification algorithm based on the results. Because the myoelectric signals fluctuated and depended on the measurement conditions in reality, we determined the criterion of each muscle's activity by statistical treatment and we evaluated the averaged IEMG signals. The AIEMG functioned as a kind of low-pass filters, and its performance was dependent on the sampling number. We investigated the results of AIEMG features and determined the optimum sampling number was 200.

Finally, we conducted some experiments on real-time discrimination of three typical hand signs. The identification accuracy was no less than 97 % with respect to any hand sign when choosing the optimum sampling number.

Experimental results proved that the proper AIEMG feature was successful in inferring the shape of hands. We have confirmed the validity and effectiveness of our proposed estimation system at last. Thus, the method to estimate hand signs has been established based on the activity of forearm muscles instead of finger muscles.

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