**6. Future directions**

Computational Intelligence in Electromyography Analysis – 258 A Perspective on Current Applications and Future Challenges

the feature vector given by (14) is shown in Table 8.

**Table 7.** Singularity recognition rate for each feature

**Table 8.** Modified recognition rate for singularity distinction

operation "insertion of a needle" were carried out.

"Straining" was improved.

**5. Conclusion** 

Then, two operators were added and the singularity distinction was performed by SOM using the feature vector defined by (14). Recognition rate for singularity distinction using

Normal 76.5 78.4 37.3 Singular 80.9 85.2 84.7 Posture 35.9 24.4 59.0 Straining 42.0 39.1 24.6 Sudden 31.5 30.3 24.7

Normal 76.5 (39/51) 72.0 (36/50) 86.0 (43/50) Singular 81.4 (192/236) 89.3 (134/150) 72.0 (108/150) Posture 25.6 (20/78) 74.0 (37/50) 44.0 (22/50) Straining 31.9 (22/69) 96.0 (48/50) 82.0 (41/50) Sudden 25.8 (23/89) 56.0 (28/50) 32.0 (16/50)

From Table 8, for the operators B and C, the singularity recognition rate for "Posture" and

In this study, a novel method for automatic identification of a surgical operation and on-line distinction of the singularity of the identified surgical operation was proposed. The surgical operation "suturing" was performed using two forceps, namely a needle driver and assistant forceps, in the built simulation box for laparoscopic-surgery. Then, the identification of the surgical operation for "suturing" and the singularity distinction of the identified surgical

As for the identification of the surgical operation, suturing was divided into six operations. The features of the operation are extracted from the measurements of the movement of the forceps, namely the amount of distortion measured by four strain gauges and the angular velocity of gimbal and stylus measured by haptic device PHANTOM Omni. Then, on the basis of the threshold criteria for the six operations, the surgical operation was identified as one of the six operations. Each surgical operation in suturing could be identified with more than 80% accuracy. As for the singularity distinction of the identified surgical operation, when the surgical operation was identified as "insertion of a needle", general distinction of normal operation or singular operation and distinction of three kinds of the states, namely "Posture", "Straining" or "Sudden" in the singular operation, were performed by the SOM using the 6-dimensional feature vector which extracted the features from SEMG. Then, the singularity of the surgical operation of insertion could be distinguished with approximately 80% accuracy on an average.

Recognition rate[%] Average absolute value Center-of-gravity Spectrum ratio

Recognition rate[%] Operator A Operator B Operator C In this study, operator for the experiments was only three persons. In order to demonstrate the reliability of the proposed automatic identification and singularity distinction method, it is necessary to perform verification of the proposed method by many operators. However, since SEMG depends on the individuals, it is considered that learning of the SOM for singularity distinction for every operator is required.

In addition, it is also necessary to extend the proposed identification and singularity distinction method for a surgical operation performed with not only a right hand but also both hands. As for this point, we are now applying the proposed identification method to a surgical operation of ligation performed with both hands, and the singularity distinction method to a thread knotting also performed with both hands.

Furthermore, construction of the system to avoid malpractice by presenting recognition of the singular operation to the operator and to provide safe endoscopic-surgery is left as future work.
