Showing 13–24 of 43 results
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
A Study on Smart Home Voice Control TerminalOpen Access
Abstract—With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control.
This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
Keywords—Arm microcontroller; Speech recognition; Wireless transceiver; Voice broadcast
Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly PatientsOpen Access
Abstract: For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.
Keywords: Soft Computing; Medical Decision Support; Triage Assessment; Fuzzy Cognitive Maps
Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination.Open Access
Abstract: The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88% increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone
Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.
Construction of a focused ultrasound neuromodulation system for the treatment of epileptic seizureOpen Access
Abstract: Thus far, Ultrasound has been proved to be useful for noninvasively stimulating brain activity and has hope to play a positive role in the treatment of neurological diseases. Epilepsy is a neurological disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, and sometimes loss of awareness. In this paper, we established a focused ultrasound (FUS) neuromodulation system for the treatment of epilepsy. We used the chemically-induced rat epilepsy model to explore the effect of pulsed focused ultrasound on epilepsy, and obtained preliminary experimental results. We have proved the feasibility of this system through experiments, which can be used in the treatment of epilepsy by ultrasound neuromodulation.
Image Segmentation-Based Face Tracking on Thermal Images for Automatic Estimation of Psychophysiological States Using Facial Skin Temperature DistributionOpen Access
Abstract: In human-machine system, human and machine need to recognize each other’s state with continuously, quantitatively and real-time property. Facial skin temperature could be measured with these properties by infrared thermography. The non-contact property is a great advantage in bioinstrumentation. Previous studies have been reported the availability of facial skin temperature for evaluation of psychophysiological states of a human such as stress, drowsiness and emotion. On the other hand, the development of the face detection and tracking techniques on thermal images are necessary for the automatic evaluation of psychophysiological states of a human based on facial skin temperature, measured by infrared thermography. The objective of this study is to establish the technique for face detection and tracking on thermal images. In this study, the algorithm consisting of three phases: (A) human detection based on inter-frame difference, (B) face detection based on image segmentation, and (C) face tracking based on temporal analysis, is proposed. As a result, the face region on thermal images could be detected and tracked with high precision. However, a part with low temperature such as the back of nasal and cheek was classified as a region other than a face.
Keywords: Thermal image; face tracking; image segmentation
Effect of Shoes on Lower Extremity Pain and Low Back during ProlongedOpen Access
Abstract: Media with 16o slope is an effective solution to reduce low back pain risk caused by prolonged standing. In this study, we examine the effect of shoes on lower back pain caused by prolonged standing for 2 hours on sloping medium. However, prolonged standing has another major risk: lower extremity pain. Many studies have shown that this risk can be affected by shoes type or characteristic. Hence, lower extremity pain risk is the main concern in this research. Two types of shoes observed in this study are Safety Shoes and Slip-On Shoes, as these are the most widely used in the manufacturing industry. Using the Surface Electromyography (S-EMG) method, the difference in Medial Gastrocnemius muscle response was measured against both types of shoes. The study showed that both types of shoes have different muscle activation values and the Safety Shoes showed greater activation. This result proves that, type of shoes may affect the amount of lower extremity pain caused while standing for 2 hours on sloping medium and Safety Shoes poses a greater lower extremity risk. Both Visual Analog Scale (VAS) and Foot Pain Questionnaire methods supported the finding. While the results of VAS method found standing for 2 hours on sloping medium has a greater lower extremity pain than low back pain risk. Foot Pain Questionnaire method indicated that the activity of standing for 2 hours over sloping medium causes a high pain on thumb toe and the back of foot. Based on this study, it can be concluded that it is necessary to design a special shoe for prolonged standing occupation on a sloping medium that can reduce the lower extremity pain risk, besides low back pain risk
Keywords: Biomechanics, ergonomics, lower extremity pain, low back pain, prolonged standing, shoes, surface-electromyography.
Calibration of Surgical Knife-Tip Position with Marker-Based Optical Tracking Camera and Precise Evaluation of Its Measurement AccuracyOpen Access
Abstract: We have been developing a liver surgery support system in collaboration with Kansai Medical University Hospital. Our surgical support system issues a warning when the surgical knife approaches a vital nerve or large blood vessel that should not be cut. It is also able to navigate the knife-tip to appropriately resect a tumor. Our system estimates the position and orientation of the surgical knife and the target organ using two distance cameras during surgery. The distance between the knife-tip and the blood vessels inside the organ is measured in real-time. In this paper, we present the details of our liver surgery support system and report the accuracy of the knife-tip positioning. The experimental results show that the position estimation error of the knife-tip is 0.3 mm and the standard deviation is 0.3 mm. The error of the distance between the estimated knife-tip positions on the neighboring grid points was 0.1 mm. This result satisfies the doctor’s surgical requirement.
Keywords: Surgical knife positioning; Calibration; Liver; Accuracy;
An Implementation of Medical Image Mosaicing System Based on Oriented FAST and Rotated BRIEF ApproachOpen Access
Abstract: Image stitching is a technique that combines two or more images that are taken from different view of the same scene to obtain a panoramic image. Image stitching is used in medical applications for stitching of X-ray images. As the traditional system of X-ray machine cannot capture the whole body structure in a single image. So, images stitching solves this problem by combining two or more x-ray images into a large view one. This paper proposes an algorithm which automatically stitches the x-ray images with overlapped region. The stitching method is based on ORB features (Oriented FAST and Rotated BRIEF features). The proposed system is designed with five stages, preprocessing, features extraction, features matching, Homography estimation and images stitching. In feature detection stage, Oriented FAST approach is used. In feature description stage, Rotated BRIEF approach is applied. The two primary parameters for measuring the stitching performance are the quality of the resultant image and the processing time. Therefore, the main objective of this paper is to produce a high quality image stitching system with low processing time. First, we compare many different features detectors. We test SIFT method, SURF method, Harris corner detector and ORB approach to measure the correct detection rate of the feature points and computation time. Second, we measure the quality of result images that produced by stitching system of different feature detection methods. From experimental results, we conclude that ORB approach is the fastest, more accurate, and higher performance.
Keywords: Biomedical images; Feature based approach ; Image stitching; ORB features ; Panorama
Optogenetics-based Neuromodulation for the Alleviation of hemi-Parkinsonian Motor Asymmetry in RatOpen Access
Abstract: The development of more effective deep brain stimulation (DBS) paradigms for Parkinson’s disease is limited by the non-specific nature of electrical stimulation. Optogenetics, with its spatial and cell-type specificity, is a potential alternative therapeutic approach. In 6-hydroxydopamine-induced hemi-Parkinsonian rats, we investigated the therapeutic values of optogenetic modulation of the subthalamic nucleus (STN) and the motor cortex. Here we report optogenetic inhibition of principal neurons in the STN significantly improved hemi-Parkinsonian motor asymmetry, measured by amphetamine-induced rotations. We also show preliminary results that revealed therapeutic improvement in motor asymmetry by single-site optogenetic excitation of the motor cortex. Although improvement from optogenetic modulations did not exceed the effects of DBS in the STN, our findings suggest that spatially patterned optogenetic stimulation of the cortex, i.e., more precise manipulation of cortical activity over larger area, should be investigated as a therapeutic approach for Parkinson’s disease.