Fasting <span>blood glucose is used as an indicator in the process of predicting diabetes risk. This research aims to, i) create a model for predicting blood glucose level using data mining algorithms, ii) a selection algorithm was used to select a feature from the correlation of the data, and iii) to compare the model's performance with the classical methods. All clinical data ware recorded and compiled in a database by hospital staff from 2014-2019. In our previous research, the blood glucose prediction model had an acceptable accuracy where 18 patient features were used as input data to the data mining process. In this research, we demonstrated that the random forest classifier and extra tree classifier algorithms have an outstanding in discarding non-critical attributes. And the process of reducing the number of those features has impacted the glycemic prediction model with higher efficiency. Seventeen machine learning algorithms are used to find the best performance models. Our results clearly show that the improved prediction model is more efficient. This experiment has shown that improvements to our proposed model were able to predict blood glucose levels with 99.69% and 99.63% accuracy for random forest classifier, extra tree classifier, and Gaussian process classifier, respectively.</span>

A new sensor system for measuring contraction and relaxation of muscles by using a PANDA ring resonator is proposed. The small scale optical device is designed and configured to perform the coupling effects between the changes in optical device phase shift and human facial muscle movement, which can be used to form the relationship between optical phase shift and muscle movement. By using the Optiwave and MATLAB programs, the results obtained have shown that the measurement of the contraction and relaxation of muscles can be obtained after the muscle movements, in which the unique pattern of individual muscle movement from facial expression can be established. The obtained simulation results, that is, interference signal patterns, can be used to form the various pattern recognitions, which are useful for the human machine interface and the human computer interface application and discussed in detail.

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We present a new result of pattern recognition generation scheme using a small-scale optical muscle sensing system, which consisted of an optical add-drop filter incorporating two nonlinear optical side ring resonators. When light from laser source enters into the system, the device is stimulated by an external physical parameter that introduces a change in the phase of light propagation within the sensing device, which can be formed by the interference fringe patterns. Results obtained have shown that the fringe patterns can be used to form the relationship between signal patterns and fringe pattern recognitions.

We propose a micro-optical sensing system for muscle contraction and movement measurement, in which a PANDA ring resonator type is used as a basic optical device called Muscle Optical Sensor (MOS) system. The MOS system consists of an optical add/drop filter which is connected to two micro-ring resonators, of which the right ring is a sensing unit and the left ring is a reference ring. The key contribution of this paper is a novel approach for measuring contraction and transfiguration of muscles by using the small scale optical device. The contraction or transfiguration of muscle is directly perturbed into the sensing unit, which changes the optical path length. These resultant changes in wavelength (∆λ) are measured and obtained by the difference of the sensing and reference signals. The experiment was conducted by using the MATLAB and Opti-wave programs, which indicated that the changes in the sensing radius were associated with the change in wavelength. The results and techniques can be used beneficially for muscle sensing applications. 

A PANDA ring resonator circuit has been applied to the measurement of muscle actions, measuring signals created by facial muscle contractions. A system, which is called the Optical Muscle Sensing System, was developed which uses sensors to measure the mechanism of facial muscle contractions and the strength of contraction and degrees of perturbation of the facial muscles that are used directly for each facial gesture. The signal data was obtained from the simulation of the facial gestures and this data was applied in the classification of the facial gesture signals of each particular gesture. Facial gestures include blinking, smiling, grimacing and various other contortions of the face which may imply emotions and are part of normal human communication. Understanding of these mechanisms will be useful and applicable to facial rehabilitation services.

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