A novel three-dimensional electric ophthalmotrope for improving the teaching of ocular movements

AIM: To develop a novel three-dimensional(3D) electric ophthalmotrope to improve the ophthalmology teaching effectiveness and evaluate the teaching value. METHODS: A 3D electric ophthalmotrope was designed by simulating the movement of the ocular and the extraocular muscles according to Sherrington’s law. The model with joint bearing was to ensure the flexibility and centripetal rotation of the simulated ball and stepper motor as the driving device. A programmable processor was used to control the motion amplitude of the stepper motor. The size of hole was set at the back of the simulated shell to limit the amount of eye movement. Afterwards, using a 5-point Likert scale, 7 experts evaluated the 3D electric ophthalmotrope’s simulation ability and precision, compared with the traditional anatomical model. In addition, the teaching effectiveness of the 3D electric ophthalmotrope was evaluated at in-class quiz and final exam in a randomized controlled trial. RESULTS: The 3D electric ophthalmotrope could be operated easily to demonstrate the eye movements with motion of different ocular muscles. The experts agreed that the 3D electric ophthalmotrope was different from the traditional model and was easier for students to understand every extraocular muscles’ movement in each evaluation index(P<0.05). Moreover, the results of teaching effectiveness showed that the 3D electric ophthalmotrope were significantly greater than the traditional model both at in-class quiz(P<0.01) and final exam(P<0.05). CONCLUSION: This novel 3D electric ophthalmotrope is better than the traditional model, which can be to improve the ophthalmology teaching effectiveness for students to understand the extraocular muscles’ movement.

Spectral and mathematical evaluation of electromyography signals for clinical use

The surface electromyography （SEMG） is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumenta- tion amplifier, filter circuit, an amplifier with gain adjustment. Fhrther, Labview^-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.