Visitsattapongse, Sarinporn

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Visitsattapongse, Sarinporn
Alternative Name
Visitsattapongse, S.
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Email
sarinporn.vi@kmitl.ac.th

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    Smart wheelchair based on eye tracking
    (2017-02-21)
    Wanluk, Nutthanan
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    Visitsattapongse, Sarinporn
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    Juhong, Aniwat
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    Pintavirooj, C.
    This project is a smart wheelchair based on eye tracking which is designed for people with locomotor disabilities. The add-on controlled module can be used with any electrical wheelchair. The smart wheel chair consists of four modules including imaging processing module, wheelchair-controlled module, SMS manager module and appliance-controlled module. The image processing module comprises of a webcam installed on the eyeglass and C++ customized image processing software. The captured image which is transmitted to raspberry Pi microcontroller will be processed using OpenCV to derive the 2D direction of eye ball. The coordinate of eyeball movement is then wirelessly transmitted to wheelchair-controlled module to control the movement of wheel chair. The wheelchair-controlled module is two dimensional rotating stages that installed to the joystick of the electrical wheelchair to replace the manual control of the wheelchair. The motion of eyeball is also used as the cursor control on the raspberry Pi screen to control the operation of some equipped appliance and send message to smart phone.
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    Biosensors for Bacillus sphaericus detection to diagnostic Pseudotumor of the lung
    (2023-01-01)
    Vongmanee, Naphatsawan
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    Visitsattapongse, Sarinporn
    In the present for diagnostic Pseudotumor in the lungs, X-ray image can help for diagnostic to the patient but could not approval the patient has got infection with bacteria type of Bacillus sphaericus. Diagnosis way of Pseudotumor in the lung by cause for this type of bacteria is very difficult because lesions are shown as pulmonary nodules or masses in which the border can be characteristically well distinguished on X-ray cannot approve infection with Bacillus sphaericus. For diagnostic need to confirm with result from laboratory and have a long time for get the result. So in this paper we will study about biosensors with molecular imprinting polymer for Bacillus sphaericus to examine the diagnosis and treatment of this disease for patient
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    Automated Bacterial Colony Counting on Agar Plate
    (2023-01-01)
    Bunkum, Manao
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    Visitsattapongse, Sarinporn
    In several fields, such as microbiology research, medical diagnostics, and food safety evaluation, bacterial colony counting is extremely important. However, the method of manual counting is time-consuming, labor-intensive, and prone to human error. This research approached these problems by using MATLAB's image processing feature to automatically count the number of bacterial colonies on agar plates. This technique effectively detects bacterial colonies from photos of agar plates by using image analysis algorithms. The images of agar plates were captured while controlling the lighting and adjusting the size to achieve the highest possible image quality. This study encompassed 10 bacterial species, achieving an accuracy of approximately 80%. This level of precision underscores the reliability and effectiveness of our automated system.
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    Optical-based foot plantar pressure measurement with application in human postural balance, gait and recognition analysis
    (2020-07-01)
    Keatsamarn, Tanapon
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    Visitsattapongse, Sarinporn
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    Pintavirooj, Chuchart
    ;
    Aoyama, Hisayuki
    In this research, we purposed the design of real-time low-cost marker-free optical-based plantar-pressure measurement with application in human postural control, gait and recognition analysis. The system consists of a series of digital cameras capture installed underneath the acrylic-top platform. Light of LED strip mounted along the side of the acrylic plate deflected by the pressure exerted between the glossy white paper installed in the top of acrylic and the acrylic plate to the digital cameras provides the color-coded plantar pressure image of the subject standing on the platform. The system can provide both static features and dynamic features. Our hybrid system consists of a series of USB cameras aligned under the acrylic-plate walking platform. The mosaic image processing is used to concatenate the captured image to increase the sensing area. Captured the image data in video mode, various dynamic parameters can be derived for further dynamic analysis. The image capturing in one specific frame can be used for static analysis. Application in human-postural measurement, gait analysis, and person identification indicate that our system is an all-in-one system for plantar pressure measurement.
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    Prototype Modeling of Bed for Bedridden Patients
    (2019-01-10)
    Bunkum, Manao
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    Reanaree, Parkbhum
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    Wanluk, Nutthanan
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    Visitsattapongse, Sarinporn
    One of the most common health risks for bedridden patient is a pressure sore or decubitus ulcers. Pressure sore developed in people who are not able to move or lying in the same position for a long period of time. To solve this problem, a caregiver must change the patient's position every two hours if possible. In addition, the caregiver already has many kinds of care work to assist the patient such as changing clothes, administering medicine, daily health checking etc. This paper presented a design model of the bed used for bedridden patients, including paralysis, individuals with impaired mobility, and the elderly. The bed consists of an automatic bed lift control system and a patient monitoring system. The bed can change the patient's position in left or right tilt by lifting the left or right side of the bed up automatically. The system came with a graphic user interface to communicate with the caregiver or physician. The patient monitoring system including, the heart rate monitor, the body temperature monitor and the blood pressure monitor which are then sent to a home or hospital server. All data can then be remotely monitored by caretaker or physician. The proposed of the bed is designed to better assist the daily life of bedridden patients and caregivers.
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    Study of ECG variation in daily activity
    (2017-12-19)
    Samona, Yanisa
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    Pintavirooj, C.
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    Visitsattapongse, S.
    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.
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    Embedded photographic tomography
    (2018-05-30)
    Fong-On, Chutima
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    Visitsattapongse, Sarinporn
    ;
    Pintavirooj, C.
    Tomographic imaging is a technique for reconnaissance of a cross-section of an inspected object without destruction and taken at a prior-known angle. Input data that were known as the projection are gathered by repeatedly capturing image through the object in a number of viewpoints. In this paper, we designed the embedded photographic tomography that can be used for a basic purpose. A number of digital cameras are attached on the rotating to capture a series of images around the object. This mimics a cone-beam projection. Here, its application is focused on the 3D modeling. In order to design the embedded system, Raspberry Pi is used as the main microcontroller that use for digital image processing in acquisition system. The Processing system will use projection data for reconstruction and 3D modeling process.
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    Indirect Measure Voltage and Exposure Time Device for Medical X-ray
    (2019-01-10)
    Bunkum, Manao
    ;
    Pintavirooj, C.
    ;
    Visitsattapongse, Sarinporn
    X-ray machine is used to create an image of diagnosing or treatment by using X-ray beam. X-ray beams are harmful to the body's cells when the radiation dose is high enough. Therefore, the X-ray machine must always be calibrated or test to ensure that the X-ray machine is accurate to keep users safe. In this research presents the design and construction of x-ray testing device. It will focus on measuring the two main parameters of the x-ray machine. Measurement of exposure time (x-ray emission time) and measurements of peak kilovoltage from the x-ray tube. This device has 2 parts, hardware and software. The hardware is the circuit consist of intensifying screen for a radiation receiver and converted energy from x-ray into light energy. The wavelength of the light emitted is in the range of 500-600 nm, then use a circuit of photodiode. It is a semiconductor device that converts light into an electric current. The electric current is a full-wave rectification waveform corresponding to the 100 Hz high voltage supply of the x-ray tube. Take the signal from the electric current to filter circuit for reducing noise. And then take the signal to the microcontroller (Arduino). Arduino is used for taking the signal to process in Visual Studio program to measure exposure time and peak kilovoltage of x-ray, which can be measured and processed immediately. The waveform is digitized, and peak detected with Arduino. The number of peaks is then converted to exposure time, i.e. one peak corresponds to 10 milliseconds. The result, exposure time and peak kilovoltage of x-ray, will show on window form application in Visual Studio program.
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    Tele-Operation of Robotic Arm
    (2019-11-01)
    Bunkum, Manao
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    Vachirasakulchai, Phakjira
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    Nampeng, Jindapa
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    Tommajaree, Ramita
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    Visitsattapongse, Sarinporn
    This research is for facilitate the healthcare worker to approach patient that located in distant areas via internet. Also, patient need not to get to the city to see a doctor. Hence, this research will reduce time and cost of traveling. This research is to create tele-control robot arm, use in the operating room, which is controlled by joystick from anywhere. It is separated into 2 parts that is hardware part to design and print 3D robot arm and joystick and software part, which is code to control robot arm also send controlling data via internet. Furthermore, this project will send real-time video from robot arm to controller side. The data is sent between robot arm and joystick by nodeMCU or in this project use two ESP32, one will receive data from joystick and send the data to another for control robot arm. In the section of sending real-time video, this project use Raspberry pi as a device for sending.
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    A Low-Cost Digital Stethoscope for Normal and Abnormal Heart Sound Classification
    (2022-01-01)
    Khoruamkid, Sorawit
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    Visitsattapongse, Sarinporn
    Heart disease is a major problem in most deaths. To conquer this situation, heartbeat sound analysis is a convenient method for diagnosing heart disease. Heartbeat sound classification remains a challenging problem in heart sound division and feature extraction. A stethoscope is a medical device widely used by physicians to listen to the heartbeat. An acoustic stethoscope operates on the chest piece to the ears of the listener. The main problem is in listening to heart sounds that the low signal level and are difficult to be analyzed. Adding electronic circuitry and software to acoustic stethoscopes will strengthen the heart rate signal and can minimize error analysis of the state of the patient's heart. Machine learning is used to efficiently analyze and classify heart sounds. Convolutional Neural Network (CNN) models and Support Vector Machine (SVM) with feature extractors were effective methods and were used in this research. First, the Phonocardiogram (PCG) files are fragmented into pieces of equivalent length. Then, we convert the PCG files to a spectrogram. The spectrogram images are fed into a convolutional neural network and support vector machine. The best result is using an Inception V3 model with the CNN classifier which has an accuracy of 0.909, with 0.948 sensitivity and 0.869 specificity.