**1. Introduction**

Recently, minimally invasive surgery such as endoscopic surgery is taking the place of laparotomy. In the field of minimally invasive surgery, a typical commercial surgical robot, such as the da Vinci system produced by Intuitive Surgical Inc., is currently in clinical use. In the robot supported surgery, master-slave system is employed. In such master-slave systems, usually motions of the master device are detected by sensors, and the slave device is controlled to follow the behavior of the master device based on the measured information by those sensors. Therefore, even the mistaken operation will be reflected.

To perform a robotic surgery, a surgeon must have considerable skill. Operation by an unskilled surgeon may result in serious malpractice. Therefore, development of a system which urges an appropriate operation to the unskilled surgeon is in demand. As described in (Tanoue et al., 2007), for training of the robotic surgery, training box or simulator has been generally used.

Recently, in order to help surgeon's dexterity, force feedback to a surgeon through the master device of a surgical robot has been studied in (Ishii et al., 2011). In order to perform safe surgery, (Ikuta et al., 2007) proposed safe operation strategies, called "Safety operation space" and "Variable compliance system" for the surgical robot. The former can prevent collision between the forceps and organs. The latter can reduce the collision force between the forceps and organs.

In addition, training systems to practice operation of surgical robot through simulation using virtual reality environment (e.g. Tokuda et al., 2009), and navigation systems which guide a surgical instrument to the targeted location during the robotic surgery (e.g. Krupa et al., 2003), have been studied.

© 2012 Ishii, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Ishii, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Computational Intelligence in Electromyography Analysis – 248 A Perspective on Current Applications and Future Challenges

To the best of our knowledge, however, a system that recognizes and points out any singularity in a surgical operation because of the inexpertness of an unskilled surgeon has not been established yet.

Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 249

divided into six operations. The features of the operation are extracted from the measurements of the movement of the forceps, and then, on the basis of the threshold criteria for the six operations, a surgical operation is identified as one of the six operations. Next, the features of any singularity of operation are extracted from operator's surface electromyogram signals, and the identified surgical operation is classified as either normal

Using the built laparoscopic-surgery simulation box with two forceps, the identification of each surgical operation and the distinction of the singularity of the identified surgical operation were carried out for a specific surgical operation, namely, insertion of a needle during suturing. Each surgical operation in suturing could be identified with more than 80% accuracy, and the singularity of the surgical operation of insertion could be distinguished with approximately 80% accuracy on an average. The experimental results showed the

Laparoscopic-surgery simulation box is shown in Fig.1. Inside of the mannequin, a rubber sheet of 1mm thickness is installed. The image of inside of the simulation box taken by the digital video camera is projected on a central monitor. An operator performs surgical operation using the two forceps, a needle driver (right hand side) and assistant forceps (left hand side) inserted into inside of a mannequin through the trocar, by looking at the monitor. The distance between the two forceps was determined based on the spatial

In this study, an operator simulates the suturing performed in a laparoscopic surgery using

**Monitor**

**Digital video camera**

**Needle driver**

**Mannequin**

**PHANTOM Omni**

relationship called "triangle formation" recommended in (Hashizume et al., 2005).

or singular using a self-organizing map: SOM (Kohonen, 2000).

effectiveness of the proposed method.

**Assistant forceps**

**2. Experimental system** 

**2.1. Simulation box** 

the simulation box.

**Figure 1.** Simulation box

In this study, to detect any singularity in a surgical operation, surface electromyography (SEMG) is employed. Our final goal is to develop such a system that recognizes and points out any singularity in a surgical operation because of the inexpertness of the unskilled surgeon on the basis of operator's SEMG signals during the operation of the surgical robot.

To this end, a novel method for automatic identification of a surgical operation and on-line distinction of any singularity of the identified surgical operation on the basis of the SEMG measurements of an operator and movement of the forceps, is proposed.

Use of the SEMG has attracted an attention of researchers as a method of interaction between human and machines. The amplitude property of waveform and the power spectrum based on frequency analysis are typical information which can be extracted from the SEMG signal.

In (Harada et al., 2010), to control a thumb and index finger of a myoelectric prosthetic hand independently, identification of four finger motions was executed using neural networks on the basis of the SEMG measurements.

In such SEMG based interaction systems, hand gestures are identified by measuring the activities of the musculature system using the SEMG sensors. It is well known that by measuring SEMG signals, not only hand gestures but also distinction between skilled person and unskilled person, and fatigue of the muscle can be recognized (e.g. Sadoyama et al., 1981, and Kizuka et al., 2006).

In (Chen et al., 2007), recognition of 25 kinds of hand gestures consisting of various motions of wrist and fingers, was performed using only two electrodes, and the high recognition rate was successfully obtained. On the other hand, (Nakaya et al., 2010) proposed a hand gesture identification method and a distinction method of any singularity in the identified hand gesture on the basis of the SEMG measurements.

(Kita et al., 2010) proposed a self-organizing approach with level of proficiency to perform stable classification of operation. (Tada et al., 2006) proposed a distinction method of unusual manipulation of a driver when driving an automobile, using the degree of deviation on the basis of the acceleration measurements.

On the other hand, as for the surgical operation, (Hayama et al., 2009) proposed an automatic classification method of four basic surgical operations using a sensing forceps made of a forceps and strain gauges. (Kumagai et al., 2008, and Yamashita, 2009) reported that in surgical operations, a difference arises between skilled surgeon and unskilled surgeon in the following points; the magnitude and direction of the handling force of the object, the manner of having surgical instrument, and surgeon's posture. (Rosen et al., 2006) proposed an evaluation method for the state transition of the forceps operation in cholecystectomy based on comparison of skilled operator and unskilled operator.

In this chapter, a novel method for automatic identification of a surgical operation and online distinction of the singularity of the identified surgical operation is proposed. Suturing is divided into six operations. The features of the operation are extracted from the measurements of the movement of the forceps, and then, on the basis of the threshold criteria for the six operations, a surgical operation is identified as one of the six operations.

Next, the features of any singularity of operation are extracted from operator's surface electromyogram signals, and the identified surgical operation is classified as either normal or singular using a self-organizing map: SOM (Kohonen, 2000).

Using the built laparoscopic-surgery simulation box with two forceps, the identification of each surgical operation and the distinction of the singularity of the identified surgical operation were carried out for a specific surgical operation, namely, insertion of a needle during suturing. Each surgical operation in suturing could be identified with more than 80% accuracy, and the singularity of the surgical operation of insertion could be distinguished with approximately 80% accuracy on an average. The experimental results showed the effectiveness of the proposed method.
