**4. Experiments and results**

The one healthy 20th generation adult man was chosen as an operator, and identification of surgical operation for "suturing" and distinction of the singularity of the identified surgical operation "insertion" were performed.

### **4.1. Method of experiments**

In the experiment, the operator repeatedly performed the suturing process (1) to (6) classified in section 3.2, under the four situations (a)Normal, (b-1)Posture, (b-2)Straining and (b-3)Sudden. The surgical operation "suturing" performed in the experiment is shown in Fig.6. Then, rate of identification of each surgical operation in suturing and rate of distinction of the singularity in the case of (4) insertion were examined.

Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 255

**Figure 6.** Suturing performed in experiment

Computational Intelligence in Electromyography Analysis – 254 A Perspective on Current Applications and Future Challenges

**Figure 5.** Experimental situations for surgical operation

Where *MAV* is an average of *MAVch* (*ch*=1,2,3).

determined as hexagon lattice type of 10 x 10.

addition, SOM was built using SOM Toolbox.

**4. Experiments and results** 

**4.1. Method of experiments** 

operation "insertion" were performed.

(a)Normal, (b-1)Posture, (b-2)Straining and (b-3)Sudden.

**Singular**

**Posture Straining Sudden**

112

123

*T*

(13)

The surgical operation of (4) insertion of a needle in suturing is classified as either normal or singular by using a self-organizing map: SOM. For classifying the surgical operation, the feature vector which is input to the SOM, is defined as follows using the features (4) to (6).

12 2 3 33

 <sup>=</sup> 

In each state shown in Fig.5, 20 features for normal operation and 60 features for singular operation (20 features for each singular operation) were pre-measured, and total 80 feature vectors defined by (13) are used for batch learning of the SOM. The size of the SOM was

In addition, k-means method was employed to divide the map into four fields, namely,

A feature vector extracted from on-line surgical operation is mapped on the map of the learned SOM, and singular operation is recognized by the distribution on the map. In

The one healthy 20th generation adult man was chosen as an operator, and identification of surgical operation for "suturing" and distinction of the singularity of the identified surgical

In the experiment, the operator repeatedly performed the suturing process (1) to (6) classified in section 3.2, under the four situations (a)Normal, (b-1)Posture, (b-2)Straining and (b-3)Sudden. The surgical operation "suturing" performed in the experiment is shown in Fig.6. Then, rate of identification of each surgical operation in suturing and rate of

distinction of the singularity in the case of (4) insertion were examined.

*MAV MAV cog MAV cog cog Xs Fr Fr Fr MAV MAV MAV cog cog cog*

, , , , , ,,,

**Normal**
