Applied Mechanics and Materials Vols. 284-287

Abstract: Finding the hip joint center (HJC) is crucial in human gait analysis since it affects kinematics and kinetics calculation of human lower limbs. Currently, the HJC location is found by either functional or prediction approaches. Prediction approaches simply use the pelvic geometric parameters to predict the HJC location. Functional approaches have been verified that they are more accurate than prediction methods with sufficient range of motion (ROM) data. However, for patients with locomotion problems to conduct full-ROM data, this paper proposes a method to fuse the functional and prediction approaches to have an intermediate result with less ROM data. The proposed method simulates large ROM data for the functional method to obtain an accurate HJC. We tested and verified the proposed method on three male subjects at the age of 25 with different body-mass indices. The experiment showed that the worst result of the proposed method was at least the same as that predicted by the prediction approach. While larger ROM data was applied, our result was significantly improved.

Abstract: This research aims to characterize the geometry of the human cochlear spiral in vivo by measuring curvature and length. Magnetic resonance imaging (MRI) was used to visualise the human inner ear in vivo. The inner ear was imaged in 12 ears in 7 subjects recruited. Visualisation of the cochlear spiral was enhanced by T2 weighting and further processing of the raw images. The spirals were divided into 3 segments: the basal turn segment, the middle turn segment and the apex turn segment. The length and curvature of each segment were measured. The measured lengths of cochlear spiral are consistent with data in the literature derived from anatomical dissections. Overall, the apex turn segment of the cochlear had the greatest degree of curvature. A detailed description of the cochlear spiral is provided, using measurements of curvature and length. This data will provide a valuable reference in the development of cochlear implantation procedures.

Abstract: Temperature biofeedback has been used in behavioral medicine for more than three decades. Traditional temperature biofeedback uses the contact sensor of a thermistor to measure one’s finger surface temperature. The goal of this study is to discover new valid sensors for temperature biofeedback. Sixteen healthy young adult volunteers (23.1±2.0 years old) practiced cognitive imagery muscle relaxation with three kinds of temperature sensors (thermography imaging, thermistor, and infrared thermopile) to measure their finger surfaces simultaneously. The temperature readings from different sensors were synchronically videotaped and converged to video files for analyses. The data were selected every half a minute from a fixed section of the muscle relaxation procedure to show the outline of the temperature variation. Twenty-eight means of skin temperature measurements from each sensor resulted in overall correlation coefficients of R=0.98 (p<0.001) between the thermistor and thermography imaging sensors, R=0.95 (p<0.001) between thermography imaging and infrared thermopile sensors, and R=0.96 (p<0.001) between thermistor and infrared thermopile sensors. These results suggest that contact and non-contact temperature sensors could demonstrate good synchronous temperature covariance in measuring finger surface temperature. This study also proposes specific setups for instrumentation of finger surface temperature monitoring.

Abstract: This paper describes the development and validation of the liquid cell smears device for early cancer detection. In this study, the liquid cell smears device with the automated production of cell slides for Liquid-based Cytology Test has been developed. To validate the Liquid cell smear device, we used each 100 samples related to cervix, sputum, urine, body fluids, and thyroid. Experimental conditions were divided into five, which were the distance of the cylinder, the moving party's ascent and descent distance, and time to stop the smears. After staining, their verification was conducted through cytologic and histologic diagnosis. As a result, we found the optimal conditions to produce slide such as cervix(GYN) at the condition 2, sputum(SPUTUM) at the condition 4, urine(URINE) at the condition 5, body fluids(BODY FLUID) at the condition 1, and thyroid(FNA) at the condition 3. Therefore, it is suggested to be possible as cytologic and histologic diagnosis.

Abstract: This study is to reconstruct the 3D model of ossicles by the smoothing technique that fitted to the human ossicles. The length of the geometry of the model was then measured. The image of human ossicles was obtained by the micro computed tomography scan. The 3D model of human ossicles was reconstructed by image processing, and then the length of the geometry of the 3D model was measured. The 3D model of human ossicles was reconstructed by image processing and by surface smoothing technique in this study. The surface of the ossicles model was smoothed by Smoothsurface. The length of the short process of the incus was 1.8 mm. The length of the long process of the incus was 5.2 mm. Based on the results obtained by this study, there was almost no difference for the measurements of the lengths of the cadaver malleus and incus between by observation and by image processing. Therefore, the image processing method was convenient and reliable. By the 3D model of ossicles, the clinician could know the structure of middle ear and could diagnose the middle ear disease more exactly.

Abstract: The medical health insurance claims application case the inspection usually relies on experts’ experience for verification and experienced personnel in charge for checking. This paper takes advantage of data-mining technology to design models and find out cases requiring for manual inspection so as to save time and manpower. By the analysis of the 20/80 principle and the coverage and accuracy ratio, a great number of periodic data (over 2 million records) are fed back to the data-mining models after repetitive verification. Also, it is discovered that to integrate the data-mining technology and feed back to different business stages so as to establish early warning system will be an important topic for the health insurance system in hospital’s EMR in the future. Meanwhile, as the information acquired by data-mining needs to be stored and the traditional database technology has limitations. Next time, this paper explores the ontology framework to be set up by semantic network technology in the future in order to assist the storage of knowledge gained by data-mining.

Abstract: The study established a set of gas detectors that helped transform exhaled breath into electronic signals, and through wave filtering, A/D transformation, and feature extraction. The fuzzy pattern classification system was then adopted to analyze and recognize the acetone level in breathing to diagnose whether someone has diabetes.

Abstract: This study proposes a simple and effective method, termed Relative Position Mapping (RPM) method, to detect the Q and S waves of an electrocardiogram (ECG) signal. This detection method employs the Finite-Impulse-Response (FIR) filter. The proposed RPM method consists of four procedures, (1) ECG signals under test are filtered by FIR and then their difference signal is obtained, (2) based on such difference signal, the search intervals for both Q and S waves are found, (3) the search intervals of both Q and S waves are mapped back to the original ECG signals under test, and (4) based on the R wave, both Q and S waves are detected. This study is examined by using 48 records from MIT-BIH arrhythmia database, each record is a 30-min ML-II ECG signals. Experimental results show that the average failed detection rate of the proposed RPM method is approximately 0.82% and their execution time is less than 1 minute for each 30-min record. The proposed RPM method is a simple and efficient detection method for detecting both R and S waves of ECG signals.

Abstract: Traditional cephalograms are X-ray films, which provide either frontal or lateral overlapped perspective medical imaging. Although computed tomography imaging provides more information in 3-dimensional anatomy, the landmarks for cephalometry are located in space which does not carry normal standards in 3-D cephalometry. The CT natural imaging method is different from X-ray in that they respectively use orthogonal and perspective projections. Thus, we cannot apply the statistical normal values gathered from traditional 2D cephometry to 3D cephalometry. This study makes use of calibrated synthesized cephalograms from computed tomography to construct a cephalometry bridge between 2-D and 3-D. In this thesis, we first review the imaging model of a specific X-ray machine (Asahi OrthoStage AUTO IIIN) by a camera calibration method. We then construct a reference system for a virtual head, and synthesize calibrated X-ray cephalograms using the volume rendering algorithm. System accuracy for the synthesis X-ray cephalograms is verified through an interactive corresponding landmark system between 2-D and 3-D. An experimental clinician was invited to manually place 17 landmarks on the X-rays and their corresponding, shuffled in random order. The systematic error, average error, and standard deviation of landmark positions are 0.15 mm, 0.97 mm, and 0.45 mm, respectively. The interactive system bridges the transformation from orthogonal 3-D to perspective 2-D cephalometry.

Abstract: A multilayer perceptrons (MLP) is proposed to aid in early detection of schizophrenia in this study. Schizophrenia is a disabling illness with unknown pathogenesis. Clinical studies have shown that there is a genetic contribution to the pathogenesis of schizophrenia. The transcription factor 4 (TCF4) gene, vaccinia-related kinase 2 (VRK2) gene as well as other five genes which were newly-identified loci of 1p21.3, 2p32.3, 8pp23.3, 8q21.3 and 10q24.32-p22.1 are among the most prominent susceptibility genes for schizophrenia. It has been hypothesized that TCF4 affects normal brain development and it has been related to different forms of neurodevelopmental disorders. Despite numerous linkage and association analyses (haplotype and meta analyses), the seven gene polymorphisms that contribute to schizophrenia remain to be elucidated. Using a haplotype block-based gene-tagging approach we will conduct an association study of thirty single nucleotide polymorphisms (SNPs) of the above seven gene polymorphisms. Artificial Neural Networks (ANNs) are widely used in prediction application. This research work will demonstrate that the MLP based prediction of schizophrenia which used genetic data (simple combination of thirty genotypic frequencies) of thirty SNPs to improve the detection possibility and accuracy with high consistency. This ANN is expected to exhibit good performance in the prediction of schizophrenia.