International Conference on Complex Medical Engineering 

Abstract: Parkinson's disease is classified as a chronic movement disorder. The incidence of Parkinson's disease is proportional to age. The status of Parkinson's disease is characterized through the Unified Parkinson's Disease Rating Scale. The Unified Parkinson's Disease Rating Scale is an ordinal scale, for which the scale is qualitatively evaluated. The inherent issue of the ordinal scale is the lack of a temporal parameter to evaluate the attributes of the movement disorder. The evaluation of the Parkinson's Disease Rating Scale requires clinical specialization, occurring in a clinical environment. Accelerometers, through the advent of miniaturization, have reached a capacity to advance the evaluation of Parkinson's disease. Tremor characteristics and temporal attributes of Parkinson's disease can be readily quantified. Accelerometer systems have been tested and evaluated for ascertaining general status, drug therapy efficacy, and amelioration of Parkinson's disease based on deep brain stimulation parameter settings. Further advance of the accelerometer characterization of Parkinson's disease attributes involves the incorporation of a fully wearable system. Such a system is possible with the integration of wireless 3D MEMS accelerometers. The device proposed incorporates a wireless and potentially wearable 3D MEMS accelerometer mounted on the dorsum of the hand. The wireless 3D MEMS accelerometer system can potentially track the Parkinson's disease status through out real time for a subject at the home-based setting of the subject. An implication of the device is a subject database can be generated quantifying the progression of Parkinson's disease. Drug therapy dosage may be optimized with quantified feedback from the wireless 3D MEMS accelerometer system. Deep brain stimulation parameters may be further refined, and the conceptual foundation for real time deep brain stimulation parameter optimization is established. Enclosed is the initial test and evaluation of the wireless 3D MEMS accelerometer system through the quantification of simulated tremor.

Abstract: This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented to classify the data into pixel-wised liver area or non-liver area. Finally, integrated morphological operations are designed to remove noise and finally delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present good classification result when SVMs are used; the other is that the combination of morphological operations with the pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and liver surgical planning system. Examples of applying the proposed algorithm on real CT data are presented with performance validation based on the comparison between the automatically segmented results and manually segmented ones.

Abstract: This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.

Abstract: Nowadays, smart materials actuated microrobots are widely used when dealing with complicated missions in limited spaces. But problems still exist in this kind of solutions, such as low locomotion speed and short operating time. To solve these problems, we propose a mother-son multi-robots cooperation system, named GSL system, which included several microrobots as son robots, and a novel designed amphibious spherical robot as the mother robot. The mother robot, called GSLMom, was designed to be able to carry microrobots and provide power supply for them. This paper will talk about the structure and mechanism of the GSLMom robot. The GSLMom robot was designed as an amphibious spherical one. The robot was equipped with a 4 unit locomotion system, and each unit consists of a water-jet propeller and two servo motors. Each servo motor could rotate 90° in horizontal and 120° in vertical direction respectively. When moving in water, servo motors controlled the directions of water jet propellers and the 4 propellers work to actuate the robot. In the ground situation, propellers were used as legs, and servo motors actuated these legs to realize walking mechanism. After discussed structures, experiments were conducted to evaluate performance of the actuators.

Abstract: Research has shown that over 90% of all adults experience dental caries, and the early diagnosis of the carious lesion has become an important aspect of maintaining dental health. Advanced diagnostic and imaging devices can be used to identify tooth damage due to caries, compensating for the low sensitivity (high false negative) rate of visual and visual-tactile inspection by dentists. However, existing systems have such a high false positive rate that dentists often do not rely on the results, instead relying on traditional visual or visual-tactile inspection. Of the existing computer-aided diagnostic systems, few if any use digital image analysis for detection and diagnosis. By using digital images and a graphical user interface, our system will give both quantitative and qualitative feedback to dental practitioners, which will address the weaknesses of existing systems. This paper details our proposed system that makes use of commercial imaging equipment commonly owned by dental practices, including an intraoral camera, to process the digital images of teeth and quantitatively assess the presence and extent of caries on the surface of teeth. We demonstrate the feasibility of using advanced image processing techniques and a C4.5 decision tree classifier to accurately identify caries from digital images.