**2.1. Simulation box**

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

not been established yet.

the basis of the SEMG measurements.

gesture on the basis of the SEMG measurements.

deviation on the basis of the acceleration measurements.

1981, and Kizuka et al., 2006).

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

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

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

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.,

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

(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

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

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

cholecystectomy based on comparison of skilled operator and unskilled operator.

measurements of an operator and movement of the forceps, is proposed.

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

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

**Figure 1.** Simulation box

As shown in Fig.2, the movement of the needle driver is measured by the haptics device PHANTOM Omni and attached four strain gauges.

Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 251

From measurements of the SEMG signals by three electrodes, the amount of distortion by four strain gauges, and the angular velocity of gimbal and stylus by haptic device, the

For identifying the surgical operation, the features of the operation are extracted from the

1

1 *<sup>N</sup> gimbal gimbal n <sup>N</sup> <sup>n</sup>* ω

1 *<sup>N</sup> stylus stylusl n <sup>N</sup> <sup>n</sup>* ω

1

1

=

where *strainch*(*n*) (*ch*=1,2…4) is measured value from each strain gauge, *ωgimbal*(*<sup>n</sup>*) and *ωstylus*(*<sup>n</sup>*) are measured angular velocity from the haptic device, and *n* represents the number of the

The features of any singularity of operation are extracted from operator's SEMG signals. The SEMG signals are measured by sampling frequency Fs=2 kHz, and Fast Fourier Transform (FFT) is performed to each SEMG signal for every N=512 sampled data, which is equivalent

After filtering the SEMG signals by the fourth order Butterworth type band pass filter with 10 Hz to 1 kHz range, the full wave rectification is carried out. In addition, for normalization, the measured SEMG signal of each electrode is divided by the maximum value of the pre-measured SEMG for each operation. Define the features as

Average absolute value: In order to perform pattern recognition, average absolute value of

1 *<sup>N</sup> ch ch n n MAV EMG N* <sup>=</sup>

1

where *EMGch*(*<sup>n</sup>*) (*ch*=1,2,3) is SEMG signal of each electrode, and *n* represents the number of

Center-of-gravity: In the case where the singular operation is performed, it is expected that change of the waveform can be observed in the SEMG signal. Therefore, as a value representing change of the waveform of the SEMG signal, the value of center-of-gravity is

( )

<sup>=</sup> (4)

=

1 *<sup>N</sup> ch ch n n St strain N* <sup>=</sup>

( )

( )

( )

<sup>=</sup> (1)

Ω = (2)

Ω = (3)

measurements of the movement of the needle driver. Define the features as follows.

**3.1. Features of the operation** 

features are defined as follows.

sampled signals.

follows.

the sampled signals.

employed, which is defined as follows.

to perform FFT every 0.256 seconds.

each electrode is often used, which is given as follows.

**Figure 2.** Sensor allocation for needle driver
