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

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

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

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

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 motion precisely.


○: Active ×: Inactive

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

#### **4.2. Criterion of muscle activity**

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

Computational Intelligence in Electromyography Analysis – 326 A Perspective on Current Applications and Future Challenges

principle. An algorithm for identifying finger motion was designed to refer the active/inactive combination described above.

Hand Sign Classification Employing Myoelectric Signals of Forearm 327

(1)

We have measured the forearm EMG signals in advance to determine the criterion for each muscle to discriminate between active and inactive signals. The criteria were separately settled with regard to several sampling numbers by observing activity of the muscles. The activity is evaluated by the peak voltage of each AIEMG signal.

Ten trials were conducted by gesturing each of the hand shapes for each sampling number.

Experimental results are arranged in Fig. 13.

Figure 13(d1\_1), for example, indicates the magnitude of 30 AIEMG waveforms detected by channel 1 with the sampling number of 1. The vertical axis represents the peak voltage of the AIEMG signal when the subject made gestures of "rock," "scissors," and "paper." It implys the activity of extensor pollicis brevis. We determined the criterion index, CI1 for channel 1 at N=1 as 0.58 V, which is illustrated by a bold line in the figure.

The activity of muscles can be estimated according to the criteria as follows. Provided that the magnitude of a measured signal is larger than the criterion, the corresponding muscle is presumed to be active. Otherwise it is considered to be inactive.

All the data regarding "rock" and "scissors" were smaller than the line, while those for "paper" were larger in this figure. That is why we could surmise that extensor pollicis brevis is active only for "paper."

In the same way, the output AIEMG signals of channels 2 and 3 are arranged as for N=1 in Figs. 11 (d2\_1) and (d3\_1), respectively. The criterion indices, CI2 and CI3 for channel 2 and 3 were determined as 0.76 and 0.61 V, respectively. By acquired AIEMG signals of extensor digitorum, channel 2 indicated the muscle is active for "scissors" and "paper." Channel 3, representing the activity of flexor digitorum profundus, confirmed that the muscle is active only in the case of "rock."

Note that these experimental data support the classification patterns of finger signs shown in Table 1.

With respect to other sampling numbers, similar features were observed as shown in Fig.11(d1\_50) - (d3\_5000). Their corresponding criteria were determined as shown in Table 2.


**Table 2.** Criterion of muscle activity

Computational Intelligence in Electromyography Analysis – 326 A Perspective on Current Applications and Future Challenges

active/inactive combination described above.

Experimental results are arranged in Fig. 13.

is active only for "paper."

only in the case of "rock."

**Table 2.** Criterion of muscle activity

in Table 1.

activity is evaluated by the peak voltage of each AIEMG signal.

channel 1 at N=1 as 0.58 V, which is illustrated by a bold line in the figure.

presumed to be active. Otherwise it is considered to be inactive.

principle. An algorithm for identifying finger motion was designed to refer the

We have measured the forearm EMG signals in advance to determine the criterion for each muscle to discriminate between active and inactive signals. The criteria were separately settled with regard to several sampling numbers by observing activity of the muscles. The

Ten trials were conducted by gesturing each of the hand shapes for each sampling number.

Figure 13(d1\_1), for example, indicates the magnitude of 30 AIEMG waveforms detected by channel 1 with the sampling number of 1. The vertical axis represents the peak voltage of the AIEMG signal when the subject made gestures of "rock," "scissors," and "paper." It implys the activity of extensor pollicis brevis. We determined the criterion index, CI1 for

The activity of muscles can be estimated according to the criteria as follows. Provided that the magnitude of a measured signal is larger than the criterion, the corresponding muscle is

All the data regarding "rock" and "scissors" were smaller than the line, while those for "paper" were larger in this figure. That is why we could surmise that extensor pollicis brevis

In the same way, the output AIEMG signals of channels 2 and 3 are arranged as for N=1 in Figs. 11 (d2\_1) and (d3\_1), respectively. The criterion indices, CI2 and CI3 for channel 2 and 3 were determined as 0.76 and 0.61 V, respectively. By acquired AIEMG signals of extensor digitorum, channel 2 indicated the muscle is active for "scissors" and "paper." Channel 3, representing the activity of flexor digitorum profundus, confirmed that the muscle is active

Note that these experimental data support the classification patterns of finger signs shown

With respect to other sampling numbers, similar features were observed as shown in Fig.11(d1\_50) - (d3\_5000). Their corresponding criteria were determined as shown in Table 2.

> N CI1 (V) CI2 (V) CI3 (V) 1 0.58 0.76 0.61 50 0.54 0.76 0.58 100 0.53 0.76 0.58 200 0.53 0.76 0.59 500 0.48 0.73 0.56 1000 0.47 0.71 0.51 2000 0.38 0.54 0.33 5000 0.22 0.40 0.23

Hand Sign Classification Employing Myoelectric Signals of Forearm 329

(3)

(2)

Computational Intelligence in Electromyography Analysis – 328 A Perspective on Current Applications and Future Challenges

(2)

Hand Sign Classification Employing Myoelectric Signals of Forearm 331

A flowchart of the gesture estimation algorithm is shown in Fig. 14. First of all, the AIEMG features are calculated by eq. (1). Next, the peak intensity, AIEMG2 of the AIEMG signal detected by channel 2 is compared with the criterion index, CI2. Then, the intensities, AIEMG3 and AIEMG1 are weighed against the criterion indices, CI3 and CI1, respectively. This process checks the combination of the measured AIEMG signals against the activation

We finally carried out the experiments of finger sign estimation based on the algorithm. Identification rate was evaluated after 40 trials were conducted for each finger sign. Several sampling numbers for AIEMG feature were investigated. Experimental results are indicated in Table 3 and Fig. 15, which show percentages of correct answers with regard to each sampling number. They suggest that N=200 is the optimum sampling number among our investigations after all. Detailed analysis clarified that larger sampling number, e. g. N=1000, deforms the signals into blunt waveforms and thus it occasionally prevents discriminating between active and inactive signals. On the other hand, the AIEMG signals contain some transient noise when the sampling number is too small. It caused misjudgment on discrimination of activity of extensor pollicis brevis, for instance, that is evaluated with electrode 1 (channel 1). It can be considered to be one of the reasons why the identification

Identify Identify Identify

patterns of muscles corresponding to the finger signs shown in Table 1.

**4.3. Finger gesture estimation**

rate of "paper" is inferior to the others.

**Figure 14.** Finger sign estimation algorithm

(4)

**Figure 13.** Discrimination between active and inactive muscle

#### **4.3. Finger gesture estimation**

Computational Intelligence in Electromyography Analysis – 330 A Perspective on Current Applications and Future Challenges

(4)

**Figure 13.** Discrimination between active and inactive muscle

A flowchart of the gesture estimation algorithm is shown in Fig. 14. First of all, the AIEMG features are calculated by eq. (1). Next, the peak intensity, AIEMG2 of the AIEMG signal detected by channel 2 is compared with the criterion index, CI2. Then, the intensities, AIEMG3 and AIEMG1 are weighed against the criterion indices, CI3 and CI1, respectively. This process checks the combination of the measured AIEMG signals against the activation patterns of muscles corresponding to the finger signs shown in Table 1.

We finally carried out the experiments of finger sign estimation based on the algorithm. Identification rate was evaluated after 40 trials were conducted for each finger sign. Several sampling numbers for AIEMG feature were investigated. Experimental results are indicated in Table 3 and Fig. 15, which show percentages of correct answers with regard to each sampling number. They suggest that N=200 is the optimum sampling number among our investigations after all. Detailed analysis clarified that larger sampling number, e. g. N=1000, deforms the signals into blunt waveforms and thus it occasionally prevents discriminating between active and inactive signals. On the other hand, the AIEMG signals contain some transient noise when the sampling number is too small. It caused misjudgment on discrimination of activity of extensor pollicis brevis, for instance, that is evaluated with electrode 1 (channel 1). It can be considered to be one of the reasons why the identification rate of "paper" is inferior to the others.

**Figure 14.** Finger sign estimation algorithm


Hand Sign Classification Employing Myoelectric Signals of Forearm 333

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

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

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

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

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

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

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

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

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

correlative experimental data of hand signs and the myoelectric signals.

**5. Conclusion** 

fingers.

features.

corresponding finger motion.

pollicis brevis and extensor digitorum.

profundus contributes only to indication of "rock."

determined the optimum sampling number was 200.

choosing the optimum sampling number.

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

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