Showing 25–36 of 183 results

  • New Image Processing Application for Life Signs Detection

    Abstract: Natural disasters such as earthquakes, landslides and tornados, occur quickly and unexpectedly leaving no time to prepare for rescue, thus causing loss of lives. Unlike the types of disasters mentioned above, this research focuses on disasters such as typhoons and tsunamis, where there is little time to prepare for the worst cases when people are stuck in their home waiting for the Search and Rescue Team (SRT). In most cases, after a disaster occurs the SRT are often unable to quickly search and rescue those who need help. This is due to many reasons. One of the most important reasons is the lack of a practical and efficient rescue system available for the rescue task. To overcome this problem, this research proposes a practical, comprehensive and efficient new search scheme to quickly detect and rescue people and facilitate the SRT task. In this new scheme a person waiting for the SRT, will post outdoors a fife sign which can be made with any familiar household items. The sign should not coincide with any existing outside signs. Once the SRT arrives on the disaster site, each drone is launched at the search spot, based on the damage area and city map information integrated into the drones. The drones mission is to automatically and quickly identify and find signs of life. This new search and rescue scheme is developed on the basis of high image processing technology. This current article proposes a rescue sign for the task.

    Keywords: Pattern recognition, rescue team, disaster, search, image processing, search and rescue

  • Changes in the Perception of Postoperative Delirium Before and After a Simulated Experience of Postoperative Delirium in Nursing Students

    Abstract: The purpose of this study is to use Unity to recreate the hallucinations and auditory
    hallucinations experienced by patients who develop delirium, and to use VR to reveal the changes in perception of delirium before and after the simulated experience of delirium to nursing college students. We used VR to recreate the ICU at night and created a video of a simulated postoperative delirium experience. The duration is 12 minutes. We set up scenes of cockroaches appearing on the ceiling, the ceiling closing in on them, people in protective clothing, and soldiers attacking at two-minute intervals. Oculus Quest was used as the head-mounted display (HMD) for viewing the VR images. The target participants were 17 students in the second to the fourth year of nursing college. The target students were asked to answer two questions before and after the viewing. The interview content was analyzed by comparing the differences in speaking time, amount of speech and content before and after the VR viewing for each student and by text mining. The results showed that using VR to simulate postoperative delirium can lead to a change in perception from understanding the inner life of patients with postoperative delirium.

  • Geometric Shape Statistical Analysis of Tibial Plafond with Ankle Osteoarthritis

    Abstract: The etiology of ankle osteoarthritis is not enough elucidated, and similar to hip and knee
    osteoarthritis, the ankle osteoarthritis caused cartilage loss and pain in daily life. However, the ankle osteoarthritis has a worse prognosis than the hip or the knee osteoarthritis. Considering the ankle osteoarthritis skeletal structure is important in selecting the appropriate surgery for the patient. Selecting the appropriate surgery will lead to an improved prognosis. X-ray images and Computed Tomography (CT) scan images are usually used to classify ankle osteoarthritis, but the evaluation of 3D bone structure is difficult and the classification based on two-dimensional measurements of X-ray and CT images may vary among medical doctors. The purpose of this study is to investigate the three-dimensional geometric deformation characteristics of tibial plafond by using statistical analysis. Deformation characteristics were found in high severity ankle osteoarthritis compared with mild group. In particular, there was a statistically significant difference in the varus deformity of the articular facet of medial malleolus and tibial plafond between stage 3B and mild group (p < 0.006), and the hyperostosis of the medial malleolus between stage 4 and mild group (p < 0.002). These results suggest that patients with severe ankle osteoarthritis have common deformity characteristics in the tibial plafond. On the contrary, there was no significant difference in the deformity of the edge of anterior and posterior tibial plafond. Such phenomena suggest that the edge of anterior and posterior tibial plafond deforms regardless of ankle osteoarthritis. This study contributes to the scientific advancement of ankle osteoarthritis surgery.

  • Audio-based Wearable Contexts Recognition System for Apnea Detection

    Abstract: Apnea or Sleep Apnea Syndrome is a condition when a person unconsciously stops
    breathing during a sleeping state for longer than a certain time. Long-term and multiple apnea events induce various impairments. However, apnea detection in hospitals is an intensive and complicated procedure and this causes highly undiagnosed and low awareness of the disease. Existing wearable devices for apnea detections mostly used heartbeat signal patterns and SpO2 levels to detect the disease, however since apnea is a respiratory impairment, it is believed that using a breathing pattern is the most straightforward approach in apnea detection. Several recent studies investigated that swallowing frequency during sleep can increase along with the apnea severity. However, the number of wearable devices using swallowing to detect apnea is very limited. Thus, this study proposes a wearable system to recognize human contexts such as breathing, heartbeat pattern, and swallowing using an audio sensor. Experiments were conducted to compare and obtain the most suitable parameters for the system such as window sizes, types of audio feature values, and classification algorithms. The prototype of the device was built and able to detect breathing, swallowing, heartbeat, oral sounds, and body movement. The result shows the best accuracy of 76.9% using 1s window size and Mel’s Frequency Cepstral Coefficient (MFCC) features in contact microphone data.

  • Evaluation of Markerless Gait Analysis Method Including Out of Camera Plane Rotate Motion During Gait

    Abstract: A RGB camera gait analysis system that does not require markers, large space, and
    preparation can provide valuable information for effective treatment decisions in clinical settings. In this paper, we propose a simple markerless gait analysis method that can measure even if the rotation angle of the foot changes. The proposed method combines OpenPose (OP) and IMU measurement data using a complementary filter as a sensor fusion method to improve the measurement accuracy of the ankle joint angle, which is predicted to be less accurate for gait with a large foot rotation angle. Nine healthy adult males walked at a self-selected comfortable speed in two different foot-progression angle gait conditions. Spatio-temporal parameters and lower limb joint angles in the two gait conditions were measured. The mean absolute error (MAE) and the coefficient of cross-correlation (CCC) were calculated to evaluate the accuracy. The spatio-temporal parameters measured by the proposed method had low MAE compared with a conventional markerless method. The similarity between the changes in the angles of the hip and knee joints and the changes in the angles measured by a three-dimensional motion capture system was found to be very strongly correlated (CCC > 0.7). The MAE of the hip and
    knee joint angles measured by the proposed method was small compared with a conventional markerless method. In particular, the proposed method was able to improve the measurement accuracy of the ankle angle by using two IMUs. The experimental results suggest that the proposed method can be used for simple and accurate measurement even when the rotation angle of the foot changes. Although the proposed method has some limitations, it has great potential as a simple and reliable gait analysis system in the clinical field.

  • Effect of the random forest with recursive feature elimination for breast cancer classification using a WDBC dataset

    Abstract:  A breast cancer is the most dangerous disease of the death cause among aged 40-55 women. We need a computer aided diagnosis system for breast cancer classification. In the previous study, the random forest which is known as an ensemble learning method was reported to be one of promising classifiers for classifying breast cancers using a Wisconsin Diagnostic Breast Cancer(WDBC) dataset. This paper presents the effect of the random forest with a recursive feature elimination for breast cancer classification on the WDBC dataset, compared to the state of the art ensemble learning techniques, such as XGBoost and LightGBM.


  • The Comparison of Two-Classes Basic Emotion Classification Methods Using a Single Heart rate change Parameter

    Abstract:  Emotion is a multifaceted phenomenon that plays a critical role in enhancing one’s quality of life by influencing motivation, perception, cognition, creativity, empathy, education, and decision-making. Additionally, negative emotions such as anger, shame, and anxiety are frequently triggered by stress, and the term destructive and threatening is used to indicate a connection between them. As a result, research into emotion recognition remains a critical issue at the moment. This study enrolled fifteen male university students. The heart rate was determined using a fingertip photoplethysmograph (PPG). The International Affective Picture System (IAPS) was used in this study to facilitate emotion changes. We used the Self-Assessment Manikin (SAM) to evaluate the subject’s emotions during the psychological assessment. As a pre-processing method, the FIR Band Pass Filter was established, and a single parameter called Heart rate change (HRC) was extracted from a PPG recording. Rather than employing complex classification techniques, we used binary classifiers such as logistic regression, Naïve Bayes, and Support Vector Machine (SVM) to distinguish between negative and positive emotions. We discovered that Naïve Bayes could provide greater than 50% accuracy and Area Under Curve (AUC) compared to the others using data from 30%, 40%, and 50% test sizes, respectively, particularly happiness (positive emotion) and anger (negative emotion). We concluded that the HRC as a single parameter could be considered the fundamental emotion classifier, though further research is necessary.

    Keywords: Emotions; Binary Classifier; SAM; Photoplethysmograph

  • Classification of Breast Pathology based on Transfer Learning by MobileNet

    Abstract:  Breast cancer is the most common cancer among women worldwide. By using artificial intelligent technique, the efficiency of cancer diagnosis can be effectively improved. However, the computer-aided diagnosis (CAD) has problems such as long training time for large-resolution pathological images and insufficient data that can be marked for training. In this article, a transfer learning model for pathological diagnosis of breast cancer is developed to overcome these problems. MobileNet was adopted to train breast pathology images under four different resolutions (40X, 100X, 200X, 400X). A transfer learning framework was established to distinguish benign and malignant breast pathologies and eight subtypes. The accuracy of the two-class model at the best magnification (200X) can reach 91.24%, and the average accuracy is 89.31%. At the same time, the multi-classification model of eight subtypes of pathological slices also achieved quite satisfactory results. It is show that the presented transfer learning framework has great potential in exploring the CAD technique for breast cancer.

    Keywords: Breast cancer; Pathological image; Computer aided diagnosis; Transfer learning.

  • A Review of Multi-sensor Information Fusion Technology Research

    Abstract:  With the development of sensor technology, multi-sensor information fusion technology has become an important research direction in the field of sensors. This paper focuses on the development of multi-sensor information fusion technology, the concepts, the levels of fusion and the main algorithms for information fusion. The development trends of multi-sensor information fusion algorithms and their characteristics are also foreseen now and in the future period.


    Keywords: Multi-sensor; Information fusion; Fusion algorithm

  • Line-Following Service Robot Using Arduino with Facial Recognition for Offices

    Abstract:  The robot working environment has changed. Robots are no longer restricted to factories and have gradually spread to urban areas. In this research work, we designed a line-following service robot using Arduino and using facial recognition to transport objects among offices. The line-following robot can proceed in its direction by following a black path; it spots the path, holds objects and recognizes and detects the picture of the target person to which the object belongs in the office. This office service robot is based on an Arduino UNO, DC motors, and batteries and is equipped with sensors, an Esp32, an IR sensor, a camera and a buzzer, since it moves among offices. Our robot can hold and transport objects, e.g., documents and letters, from the source to a destination by following a path and detecting and recognizing the target person who should receive the object. In addition, the buzzer within the robot will alarm and notify the target person with a specific sound that will be heard when the robot recognizes the target (him or her).

    Keywords: Arduino, IR sensor, Esp32, Camera, Microcontroller, Line-following robot, Actuator, Facial recognition, Service robot, Buzzer.

  • Magneto-optical images for nondestructive inspection of plant steel structures using deep generative models

    Abstract:  Measures against deterioration of infrastructures that were built during the high economic growth period are facing significant challenges with regard to the maintenance of infrastructures in Japan. The development of optimal nondestructive sensing and imaging technology according to the material and structure of buildings is underway to contribute to efficient and reliable maintenance of infrastructures. However, owing to the large number of materials and structures used for buildings, as well as the types of defects to be targeted, many basic studies are yet to reach the stage of practical use. In this study, we developed a magneto-optical (MO) sensor in order to visualize a “crack” in the plant steel structure and automatically detected the defects in the plant steel structure by performing deep learning on the MO image obtained. As a pretreatment for detecting anomalies in defects using the AI, we focused on the nondestructive inspection using MO imaging and performed an unprecedented image filter processing. As a result, automatically evaluation the several types of MO images using AI, the accuracy of defection identification was improved.

    Keywords: artificial intelligence; variational autoencoder, nondestructive inspection; magneto-optical imaging

  • Object Searching Robot Controlled by Edge-AI

    Abstract:  This study proposes and develops an edge-AI-based autonomous mobile robot based on open-source software. The robot is capable of voice and object recognition; it can detect and approach an object specified by a user’s voice. Because the robot is controlled by voice commands, the user can control the robot intuitively. In the present study, we used a robot operating system to facilitate the development. All functions, including voice recognition, object recognition, and motor control, were implemented in the edge AI computer based on open-source software. We conducted preliminary experiments to verify the performance of the developed system.

    Keywords: Mobile robot, Edge-AI, Open-source software, Image recognition