In current domestic research on laparoscopic training, researchers usually consider instrument movement path in the hand-eye coordination relationship. However, they ignore the information contained in visual cues by which could guide and control instrument movements. Studies in other areas have shown that trainers can improve their perceptual-motor skills by gaze training. This paper was designed to examine the effectiveness of eye gaze tracking technology in laparoscopic training and to analyze gaze strategy of the subjects in different training methods. The Tobii X1 Light Eye Tracker was used to track the gaze position of subjects when they were performing the two-handed transferring task in box trainer, and to obtain parameters related to gaze strategy including the efficiency of task completion, as well as visual search, visual processing and observation transfer analysis based on Markov chain model. The results showed that the completion time during the last training in gaze training group was decreased by 101.5 s comparing to the first training. Compared with video training group, gaze strategy of gaze training group has a significant change, such as fixation and saccade duration rate was increased by 38%, fixation duration on target area was increased, and saccade amplitude increased by 0.58°, and the probability of the fixation point transferring to equipment decreased by 15%. The results demonstrated that eye gaze tracking technology can be used in laparoscopic training, and can improve the subjects’ skills and shorten the learning curve by learning gaze strategies of experts.
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.
The aim of this study is to evaluate the effect of laparoscopic simulation training with different attention. Attention was appraised using the sample entropy and θ/β value, which were calculated according to electroencephalograph (EEG) signal collected with BrainLink. The effect of laparoscopic simulation training was evaluated using the completion time, error number and fixation number, which were calculated according to eye movement signal collected with Tobii eye tracker. Twenty volunteers were recruited in this study. Those with the sample entropy lower than 0.77 were classified into group A and those higher than 0.77 into group B. The results showed that the sample entropy of group A was lower than that of group B, and fluctuations of A were more steady. However, the sample entropy of group B showed steady fluctuations in the first five trainings, and then demonstrated relatively dramatic fluctuates in the later five trainings. Compared with that of group B, the θ/β value of group A was smaller and shows steady fluctuations. Group A has a shorter completion time, less errors and faster decrease of fixation number. Therefore, this study reached the following conclusion that the attention of the trainees would affect the training effect. Members in group A, who had a higher attention were more efficient and faster training. For those in group B, although their training skills have been improved, they needed a longer time to reach a plateau.