Showing papers presented at "Biomedical Engineering International Conference in 2017"

Image enhancement on digital x-ray images using N-CLAHE

Abstract: Digital chest radiography offers many advantages over filmbased radiography, such as immediate image display, no film processing and room storage, wider dynamic range and lower radiation dose. In general, a raw X-ray image acquired directly from a digital flat detector contains poor quality of image, which may not be suitable for diagnosis and treatment planning. Therefore, a pre-processing technique is usually required to enhance image quality. This paper presents an improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement. The proposed technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images. The proposed approach was tested using a radiographic survey phantom and a radiographic chest phantom and compared with conventional enhancement methods, such as histogram equalization, unsharp masking, CLAHE. The results show that the proposed N-CLAHE method yields great improvement on the pre-processing correction for digital chest radiography.

Face recognition based on facial landmark detection

Abstract: This paper presents a novel technique for face recognition based on facial landmarks extracted automatically. Our landmarks are those associated with eyes mouth and nose. With the extracted landmarks, the area triplets and the associated geometric invariance are formed. We opt to use area and triangle confined within the triangle as the invariance. To bypass the perspective constraints, we take the face image with high focal length and at the farther distance. Orthogonal projection and Euclidean transformation are then assumed. As area is relative invariance under Euclidean transformation, the absolute area ratios between consecutive area triples are applied. Our purposed algorithm is tested successfully to identify person and could be a promising technique for facial recognition.

Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features

Abstract: Early identification of breast cancer is important for reducing the mortality rate. A common screening and detection technique for breast cancer is mammography. Though Mammography is now considered as the benchmark technique for the early screening and diagnosis of breast cancer, it utilizes harmful ionizing radiations, namely, X-Rays. Moreover the procedure is quite uncomfortable, painful and embarrassing for women, making it less attractive as a preventive screening tool. These disadvantages of mammography can be overcome by using ultrasound imaging technique. Ultrasound is normally considered safe and can be used in soft tissue such as breast. However the quality of the image is compromised due to the predominance of speckle noise. This paper focuses on developing an algorithm for an automatic segmentation and classification of breast lesions from ultrasound image in which the speckle noise was reduced using Tetrolet filter and breast lesions were automatically segmented by using statistical feature based active contour method. After segmentation, for the classification of breast lesion, totally 40 features such as 15 textural, 21 morphological and 4 fractal features were extracted from the images. Optimal features were selected to increase the classification performance by using ReleifF algorithm and 10 best features were taken into account for feature ranking. These features were used to classify the lesions from breast ultrasound images by using Support Vector Machine (SVM) with polynomial kernel for the combination of texture, morphological and fractal features from the Tetrolet filter. This method would help the radiologist to detect and classify the lesions automatically.

Data augmentation methods for machine-learning-based classification of bio-signals

Abstract: Data augmentation methods for bio-signal classification are proposed. These methods improve recognition performance of human mental states showing intrinsic motivation from brain wave. Conventionally, data augmentation is used to image recognition research. Scaling, rotation, and distortion are applied to the original images to increase examples for machine learning. However, these augmentation methods are not effective for use with biological signals, as they involve spatial manipulation designed to represent the fluctuations of natural images. In the present study, we proposed four novel methods for data augmentation of biological signals. These methods are designed to represent variations inherent to bio-signals, especially for event-related signals. Electroencephalogram (EEG) data from participants engaged in an intrinsic motivation task were utilized to evaluate the feasibility of the proposed data augmentation methods. Our findings demonstrated that the proposed methods are particularly effective for improving prediction accuracy in small datasets.

A study of integration Internet of Things with health level 7 protocol for real-time healthcare monitoring by using cloud computing

Abstract: Thailand will fully enter aging society in 2025 has a major impact on the rise of patients in each country. There is much organization has tried to develop technology for support aging society. Internet of Things is one of them and it can be connected device to device. Currently, there are many medical devices in Internet of things such as wearable device, digital blood pressure device, blood glucose meter etc. The data from these devices have been used to accurate treatment patients. From the study, related literature. Researcher realizes the importance of accurate medical data transfer and can be supported amount data. This paper present conceptual framework of integration Internet of Things with Health Level 7 protocol for support real-time healthcare monitoring by using Cloud computing. The objective of the conceptual framework is to help elderly or people can check health care with themselves anywhere anytime by using the medical device in Internet of Things. These data real-time storage to Cloud computing with JSON language. So, public health and hospitals can use information for treatment patients or give advice about healthcare through web service with XML language according to Health Level 7 standard.

Noisy speech training in MFCC-based speech recognition with noise suppression toward robot assisted autism therapy

Abstract: Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders. Autistic children experience challenges in three important areas: social communication, social interaction and repetitive behavior. Robots have become tools to support therapists in autism therapy. Toward integrating social interaction and communication in robot-assisted autism therapy, the robot should have speech recognition ability that can be used in noisy environment. This study investigated the potential of using noisy speech training in MFCC-based speech recognition system with noise suppression toward robot-assisted autism therapy. Experimental results with clean speech training on Japanese speech database suggested that MFCC with noise suppression technique could provide the improvement with significantly higher recognition accuracy than MFCC. With noisy speech training with noise type found in the environment, the performance can be improved even more.

Neurofeedback game for attention training in adults

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a disorder of performance with core symptoms of inattention, hyperactivity or impulsivity. In adults, hyperactivity may decrease, but struggles with inattention or impulsivity may continue. Neurofeedback game can be used as an alternative approach to enhance attention. In this pilot study, we developed a neurofeedback game for attention training in adults. The game was designed with key elements to help users prolong and increase their attention levels. We conducted a pilot study at King Mongkut's University of Technology Thonburi, THAILAND with seventeen adults. Participants were divided into two groups: high-risk and normal, based on their initial assessment of ADHD symptoms using Adult Self-Report Scale V1.1. After 20 sessions of 20-minute neurofeedback game, our results suggest that this type of therapy could be effective as an alternative treatment. We found that the average of attention level, the time retained at elevated attention levels, the time users used to refocus during the game, and their performance on Test of Variables of Attention (T.O.V.A.), which measures person's attention, have been improved by the end of the training for the high-risk group. Hence, our game could be an additional approach to encourage adults to improve their attention in a fun and effective way. We expect that the outcome of our training could benefit the users on their daily life because users have learned to regulate their attention through our neurofeedback game.

Evaluation of performance on preterm birth classification using single wavelet-based features of EHG signals

Abstract: In this study, wavelet-based features of electrohys-terogram (EHG) recordings obtained from pregnant women are applied for preterm birth classification. The wavelet-based feature of EHG signal, Δι, is determined from a difference between the logarithms of variances of detail coefficients of EHG signal corresponding to two consecutive levels, i.e., level I and level 1 + 1. The performance on preterm birth classification using single wavelet-based features is reevaluated using the receiver operating characteristic (ROC) analysis. The computational results show that best performance on preterm birth classification is achieved with the area under ROC curve of 0.7386 using the wavelet-based feature Δ 6 of EHG signals recorded after the 37th week of gestation.

Cognitive training using immersive Virtual Reality

Abstract: Normal adult becomes memory decline when increasing age. Memory decline can change from normal aging to mild cognitive impairment (MCI) and then Alzheimer's disease dementia. In order to reduce the risk of dementia, the cognitive training or brain training is needed. Cognitive training can stimulate the ability of normal person's memory for keeping ability of memory prompt when increasing age. Virtual Reality (VR) technology can be used to establish VR application for cognitive training. This paper proposes VR application for cognitive training that uses a head-mounted display for a full immersive device with VR controllers. This VR application is designed for training in visual memory and visuospatial under the psychiatrist's guidance. There were 45 participants in this experiment. All participants were tested memory ability of remember things and places from first practicing and evaluated satisfaction after using our VR application from satisfactory form. This experiment can help to determine the difficulty level in the next training and suitability of activity. The time duration of recall memory related to difficulty level in VR application (correlation coefficient, p = 0.379, 0.010). Furthermore, the satisfied evaluation of participants using VR application was “satisfied” and “very satisfied” in each difficulty level in VR application that Cronbach's alpha = 0.743.

Classification of thyroid ultrasound images based on shape features analysis

Abstract: Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying thyroid nodule based on shape feature analysis into two classes, i.e. round to oval and irregular classes. The proposed scheme is tested on 165 thyroid ultrasound images consisting of 61 round to oval images and 104 irregular images. The process is started by filtering image as the preprocessing step followed by segmentation process using active contour and morphological operation. Shape analysis is performed by extracting seven geometric features and 14 moment features. These features are then selected by using correlation based feature selection (CFS). Three selected features are classified using linear support vector machine (SVM). The classification results achieved the level of accuracy, sensitivity, specificity, PPV and NPV at 91.52%, 91.80%, 91.35%, 86.15%, and 95%, respectively. These results indicate that the proposed scheme successfully classified the shape of thyroid nodule.

Analysis of microcalcification features for pathological classification of mammograms

Abstract: In this study, we aim to analyze the quantitative features for characterizing microcalcification clusters on mammograms for pathological classification into benign and malignant classes. Our database includes 101 cases: 48 cases of benign and 53 cases of malignant with biopsy proven. Two views of mammogram images, Cranial Caudal (CC) view and MedioLateral Oblique (MLO) view were used in our experiments to extract the promising features. A total of 72 features were extracted from each view for analysis. Performances of the considered features are evaluated with an application to mammogram classification. A deep neural network (DNN) classifier was used as a supervised learning in the classification. Performances of the features are evaluated by three measures: Sensitivity, Specificity, and Accuracy. The results showed that for the CC view, Mean of Areas of microcalcification spots, Mean of Major Axis Lengths of microcalcification spots performed better than the other features, followed by Mean of Minor Axis Lengths of microcalcification spots and Mean Diameter of all microcalcifications; for the MLO view, Mean of Perimeters of microcalcification spots, and Mean Diameter of all microcalcifications performed better than the other features, followed by Mean of Contrasts of microcalcification spots, and Diffuseness (standard deviation of inter-distances of all microcalcifications in the ROI). The Mean Diameter of all microcalcification spots is the good feature for both CC and MLO views.

Performance of a QRS detector on self-collected database using a handheld two-electrode ECG

Abstract: This paper presents a development of a handheld device for single-channel ambulatory ECG measurements. Our ECG device is designed for easy-to-use. ECG signal will be recorded for 1 minute by placing both thumbs on two dry electrodes and we have used this device to collect ECG signals from 50 volunteers. An ECG database is created from the collected ECG signals with self-annotated locations of QRS complex. This process is indispensable for developing the algorithm and for testing the performance of ECG analysis. The self-collected database has been used to evaluate the performance of a simple slope-based QRS detection algorithm, which had a relatively high accuracy on MIT-BIH Arrhythmia Database from our previous work without preprocessing filtering. As a result, the performance of the same QRS detector on our 50 short-term ECG records has an excellent sensitivity of 99.74%, but a moderate positive predictivity of 94.25% because of high level of EMG and movement artefacts from thumbs. After applying preprocessing filter (band pass 9–20 Hz), the positive predictivity is significantly improved by about 4%. This work showed that for the development of QRS detector for a handheld two-electrode ECG device in our case, the detection algorithm should be optimized by using a device-specific database because of some particular noise characteristics of ECG signals recorded from the handheld ECG device.

Classification of margin characteristics from 3D pulmonary nodules

Abstract: Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The main objective of this research is to classify different margin types of pulmonary nodules by observing the 3D structure. Nodule candidates from 2D lung CT slices are segmented firstly and then stacked to form a 3D image. Geometric features of the 3D nodule are extracted and fed into the support vector machine (SVM) classifier to classify the margin types. The proposed method can provide the classification accuracy of 90.9%.

EEG-based communication system for patients with locked-in syndrome using fuzzy logic

Abstract: Patients who are conscious and aware of their environment but are physically disabled are known to have Locked-in Syndrome. The causes for this medical condition include traffic accidents, drug addiction and brain clots. There are some available solutions nowadays to help them communicate but the down side is the requirement for physical training which can be both time and money consuming. The main objective of this project is to help these patients communicate and engage more effectively in their daily life. In this paper, an Electroencephalogram (EEG)-based communication system is developed to facilitate communication of these patients with their caretakers. The implementation is composed of both hardware and software. The hardware consists of a 14-channels EEG module and a tablet. The software parts are: processing algorithm, online database and an android application. The EEG module on the patients' scalps keeps reading brainwaves continuously. Meanwhile, the tablet in front of them displays six basic needs, namely, food, water, washroom, help, sleep and entrainment. When the patients focus on a specific need, it will be detected when it matches with a predefined reference in the processing algorithm. The processing is done using fuzzy logic pattern recognition based on eye movement and color detection. The database acts as a two-way communication link between the patients and their caretaker. As the message will be sent through it to the android application-which is installed in the caretakers' phones-in the form of a pop-up notification. Interchangeably, a response message can be sent by the caretakers to state they are on their way for instance. Besides, the tablet will generate a voice message to inform the people around the patients about their need. A prototype system has been developed and successfully tested.

Tracking control of a robotic orthosis for gait rehabilitation: A feedforward-feedback control approach

Abstract: In this study, the trajectory tracking control problem of a 2 degrees of freedom robotic orthosis for the lower limb rehabilitation is discussed. The robotic orthosis is actuated by a pair of self-made pneumatic artificial muscles (PAMs) in an antagonistic configuration. A second order plus dead time (SOPDT) linear model is chosen for describing the antagonistic actuator behavior. In order to enhance the trajectory tracking performance, a modified feedforward-feedback control scheme which utilizes the known future information of the desired trajectory is employed. The effectiveness of the proposed strategy is verified by experiments in different gait cycles.

Online state space filtering of biosignals using neural network-augmented Kalman filter

Abstract: Since its inception, the Kalman filter, which represents the optimal estimator for linear, Gaussian state space models, has been adopted for a wide array of practical applications due to its efficient, recursive formulation. At the same time, the field of nonlinear time series analysis has produced powerful methods for filtering time series with deterministic dynamics. These nonlinear filters differ significantly from linear filters, because they transform the time series via delay embedding and exploiting geometric features in delay space. However, they are not capable of operating recursively like the Kalman filter. In this paper, we propose a state space filter based on the delay embedding principle, but capable of online estimation. This is achieved by formulating the nonlinear delay space filter as a state estimation problem, which can be solved using the extended Kalman filter. In order for this reformulation to work, it is necessary to approximate the dynamics of the time series. For this purpose, we use a feed-forward neural network. By embedding the neural network weights in the Kalman filter state, we are able to simultaneously estimate the hidden dynamics of the time series and perform online state space filtering. We present preliminary performance estimates of our online state space filtering approach obtained from tests with artificial biomedical time series.

An experimental procedure for quantitative evaluation of spectral resolution of spectrometer-based spectral domain optical coherence tomography

Abstract: Optical coherence tomography (OCT) is an emerging optical technology that is capable of non-invasive cross-sectional imaging of biological tissues at high-speed, highresolution, and high sensitivity. OCT has been proven to be a potential tool for medical diagnostics and biomedical researches. One important parameter that governs an imaging depth of spectrometer-based spectral domain OCT (SD-OCT) is the spectral resolution of a spectrometer used in the system. Here, we have developed a laboratory procedure to evaluate the spectral resolution of a spectrometer during its alignment process. We have implemented the procedure to our custom developed spectrometer to improve its effective spectral resolution and hence the imaging depth of the SD-OCT system. The optimized spectral resolution of the spectrometer is necessary for extending an imaging depth of SD-OCT, which would be useful for many medical diagnostics, such as skin imaging and retina imaging.

Identifying a radiomics imaging signature for prediction of overall survival in glioblastoma multiforme

Abstract: This paper identifies a MR imaging radiomics signature for prediction of overall survival (OS) in patients with glioblastoma multiforme (GBM). A fully-automatic radiomics model is presented, including automatic tumor segmentation, high-throughput features extraction, features selection, and multi-feature signature identification. The automatic GBM segmentation method employs a random forest classifier with a CRF spatial regulation where the importances of the multi-modality features are considered. After feature selection, a 4-feature radiomics signature is identified based on training data and further confirmed on independent validation data. The proposed signature succeeds to stratify patients into prognostically high-risk and low-risk groups, indicating the potential to facilitate the preoperative patient care of GBM patients.

Electrocardiographic criteria for the diagnosis of left ventricular hypertrophy based on data mining

Abstract: Left ventricular hypertrophy (LVH) is the main manifestation of cardiovascular disease in patients with hypertension, and is an independent risk factor for multiple cardiovascular complications. So the medical researchers attach enough importance to it in aspect of clinical practice. Electrocardiograph (ECG) has the unique advantages of simple operation and low price, which support its widespread use for screening LVH. So far, the American Heart Association has approved 37 different ECG criteria, but it is widely believed that all of these criteria show problems of high specificity but low sensitivity. Therefore, finding a new ECG criterion which has better diagnostic value becomes a research hotspot. The purpose of this study is to propose a new method for the selection of ECG diagnostic criteria based on the method of data mining. The process of algorithm research including ECG signal preprocessing, feature extraction, feature selection, and diagnostic criteria establishment. The results show that the criteria proposed by our method significantly improve the sensitivity (up to 80%) and the overall accuracy.

Study of ECG variation in daily activity

Abstract: Electrocardiogram (ECG) records electrical activity of the heart spreading through the heart muscle to make the heart contract. Recently ECG has been captured attention as biometric feature due to its uniqueness and large reliabilities for human identifications. In this study we aimed to verify the conservative ECG of human in their activities to ensure whether it is suitable to be used as biometric devices. Experiment studies involved 6 participants of which the age ranges is between 21 and 23. We test the robustness of ECG under various situation including health condition, emotional state and heart rate variation. The recorded ECG signal is forwarded for analysis using Matlab. Correlation coefficient of ECG Fourier transform is used as criterion to validate the ECG robustness. The result indicates that ECG is not stable and seems to vary with daily activity and emotional state. This will hampers ECG to be used as Biometric.

A constrained nonlinear optimization solution for 3D orientation estimation of the human limb

Abstract: The aim of this research is to improve the methodology for human limb attitude estimation using wearable sensors. The orientations of these limb segments are measured using inertial/magnetic sensor modules. Such sensor modules typically contain a triad of orthogonally mounted accelerometer and magnetometer. The accelerometer is used to measure the gravity vector that is relative to the coordinated frame of the sensor module. Magnetometer serve a similar function for a local magnetic vector. Based on these two observations, we can formulate a constrained nonlinear orientation estimation of the human upper kinematics, based on the Wahba problem formulation. Several algorithms had been proposed earlier for solving this problem but all those currently solutions focused on minimizing the lost function (or cost function) numerically. As a result, these methods led to errors owing to unexpected noise, internal and external impacts such as, drift, vibration, force changes, etc. Hence, to mitigate this issue and enhance the effect of the solutions in tackling Wahba problem, a reachable workspace is defined in this work in addition to the current research algorithms.

An asynchronous SSVEP-BCI based on variance statistics of Multivariate synchronization index

Abstract: In this paper, a threshold calculated from simple variance statistics of Multivariate synchronization index (MSI) was proposed to construct an asynchronous Brain-computer interface (BCI) paradigm based on steady-state visual evoked potential (SSVEP). Compared with simplified Linear discriminant analysis (LDA) classification, the threshold helped system judge control/idle state more effectively and robust. And it was used in control a robot which with obstacle avoidance, helping to reduce the mental burden with the shared control strategy. To some extent, it can be explained, the use of simple methods can also achieve better control in human-computer interface and integration.

In-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in ultrasound acquisitions of the liver with two specialists in Internal Medicine

Abstract: The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice.

Can potentials of unbalanced complex kinetics of heart rate variability estimate drowsiness

Abstract: Several investigations have shown that most traffic accidents are due to drowsy driving. Much research has been carried out to address this issue. The authors of one study were able to measure the drivers' heart rate variability and estimate their drowsiness. In this study, we investigated the relationship between the drowsiness and physiological condition of the drive in the driving simulator environment by quantifying autonomic nervous system activity using “potentials of unbalanced complex kinetics” (PUCK) analysis of heart rate variability and conventional frequency-domain analysis using the Karolinska sleepiness scale (KSS). Both frequency-domain and PUCK parameter values had significant statistical differences for almost all subjects in relation to drowsiness: no drowsiness (mean KSS scores 5.5 and 7.5). Furthermore, the classification ability of PUCK analysis was superior to that of frequency-domain analysis. Therefore, PUCK analysis of heart rate variability may be useful for assessing drowsiness while driving.

Dynamic foot plantar measurement platform using optical sensors

Abstract: Foot plantar pressure is the interface pressures between the foot surface and the supporting surface during daily activities. Pressure information provides valuable insight in gait and posture research for diagnosis diseases, footwear design, sport performance, injury prevention and other applications. In this paper, a dynamic foot plantar pressure measurement platform is proposed suitable for measuring dynamic plantar pressure in real-time with high accuracy and reliability. A Platform has made from steel structure covering with 15 mm acrylic plate with LED strip on the top and surrounding by 3 mm black acrylic to block undesirable light. The optical sensors, the infrared cameras, have connected on Raspberry Pi to process and record plantar pressure frame in avi file using OpenCV library.

Hand exoskeleton for continuous passive motion postoperative rehabilitation

Abstract: To achieve the sufficient treatment, the rehabilitation is a main key to regenerate the hand performance. Due to one occupational therapist can rehabilitate one patient at a time and the number of patients is increasing, this causes occupational therapist overload. HEXO is a device for passive support of the joint movement by controlling through the software. It performs on four fingers which are index finger, middle finger, ring finger and little finger. The performance has main mechanic as transmission sling that actuates the system and is integrated with PID position control through interface. There are twelve degrees of freedom: flexion and extension on each finger. Importantly, there is an emergency button that allows the exoskeleton to stop.

Design and development of massage therapy device for arm

Abstract: Currently, lateral epicondylitis of the elbow (tennis elbow) has causes of daily activity such as housework, sport, or use computer overtime. This problem affects 1% to 3% of the population. The symptom of tennis elbow is a sore elbow or a sore to the arm leading to nerve compression syndrome. The aim of this study is to design and develop a massage therapy device for arm in order to help tennis elbow patient and myofascial pain. This paper uses ischemic compression massage for designing massage therapy device and the distance of pressing on the muscle of the arm according to physician and rehabilitation doctor. This study of massage therapy device investigates and compares the force and weight between different massage modes, evaluates pain score rating by facial scale from 10 healthy subjects. The findings show that the massage therapy device for arm can work as desired.

Electric wheelchair simulation from unity 3D for controller test

Abstract: A capable of easy simulation program to help the design of a wheelchair is needed, because the design of wheelchair easy to move needs much time and cost. In this paper, we propose a capable of easy simulation program to help the design of a wheelchair. In our program G-code is used to control 3D model of electric wheel chair in computer numerical control (CNC). In addition, the 3D models derived from the initial design can be used to express the overall appearance of the design. However, it is not enough to make those things because the design is fully functional and it is not possible to know the problems that might arise from the wrong design. The design of the control system also affects the design improvement and the assembly of the electric wheelchair. We have to design it again with the form and new assembly. This will cause additional time and expensive materials in order to prevent these problems. The computer simulation is able to be aware of potential problems with the design of the error. It allows us to perform tasks, test control systems and analyze structures. In addition, simulation can also be used to practice control. There is no need for the real of electric wheelchairs.

Hydrogel-supported bilayers for studying membrane protein functions

Abstract: In vitro reconstitution of membrane proteins in lipid bilayers is thought to be an effective approach for understanding their function. Although a number of methods have been developed to create planer lipid bilayers and liposomes, each method has specific disadvantages. In this paper, we utilized hydrogels and reduced their disadvantages while retaining their original advantages. First, a lipid bilayer was formed between the surface of a hydrogel and a buffer solution with the contact method. Since the hydrogel supported the bilayer, stability is expected to be improved. Capacitance measurements evaluated the bilayer formation with an electrophysiological setup. The amplitude of the current responses reached up to 160 pA, suggesting that the surface area of the bilayer was approximately 8.3 × 103 μm2 (diameter of 50 μm). Second, the lipid bilayer was formed on patterned agarose using the self-spreading method. In this situation, the agarose works as a spacer between the membrane and substrate. Membrane formation was evaluated with fluorescence recovery after the photo bleaching method. Third, for future studies with membrane proteins, liposome formation was evaluated in various conditions, and we confirmed that liposomes could be formed in an in vitro protein synthesis solution.

Time-lapse changes in EEG-EMG coherence during weak voluntary contraction of the tibialis anterior muscle

Abstract: In this study, we investigated the relationship between EEG-EMG coherence and time-lapse changes during weak voluntary contraction of the tibialis anterior (TA) muscle. Eleven healthy men (21–29 years old) participated in the study. The subject was directed to contract his TA muscle for one minute at 10%, 15%, or 30% of maximum voluntary contraction by flexing his right tarsus. Each contraction was randomly performed and repeated five times. The relationships between each contraction level and the epoch number (1 epoch = 12 seconds) were analyzed. Peak coherence values increased significantly over time (p < 0.05). Moreover, the peak coherence value of the beta band changed significantly with time (p < 0.05). For contraction at 30%, the peak coherence appeared in the gamma band on the first and third epochs, while it was seen in the beta band on the second, fourth, and fifth epochs. A decrease in mean power frequency was observed over time. We observed significant differences in the voluntary contraction level (p < 0.05) in each epoch (p < 0.01). The fact that muscle fatigue and the maintenance of muscle contraction leads to synchronization of motor units suggests the activation of afferent feedback.