We developed a small coil and oscillator system for the hyperthermia of magnetic nanoparticles. The inner diameter and the length of the coil are 50 Φ and 60 mm, and we used them for the mice experiments. The frequency is 100 kHz, and the magnetic field is higher than 34.6 kA/m for the 5-Turn coil. Some experimental result is positive, and therefore we try to extend the system size for the human body in the same frequency and the same magnetic field intensity. Using the scale law in physics, and the coil is 500 Φ diameter, 600 mm length, and 5-Turn, but the magnetic field intensity and the frequency are the same as the present system. We design the instrument parameters. Fortunately, these design parameters are realistic to make a large device using the present electric parts in the market.
VR has been applied to medical treatment and welfare, and there are more and more opportunities for the elderly and recuperators to use VR. However, it is difficult for the elderly and recuperators to wear head-mounted display (HMD) because of the physical burden. In addition, immersive display that covers the user's surroundings is difficult to introduce in medical and welfare environment due to the large size of the equipment. These challenges are particularly significant for bedridden patients. Therefore, we propose a VR image presentation system for bedridden patients that can be used at the bedside without the burden of wearing a device. Through the subject experiments, we confirmed the usefulness of the proposed system regarding the presence and simulator sickness. In addition, we investigated the method of presenting VR images in the lying position, and showed that it is effective to present VR images horizontal to the eyes. Furthermore, we obtained the design parameters of the display shape considering the head posture in the lying position. From these findings, we obtained a guideline for system design considering head posture restrictions for bedridden patients.
To understand the underlying neuronal representation for object recognition, we designed this study to investigate the changes in N1, the first negative event-related potential component, accompanied by object discrimination learning. Subjects were asked to train themselves to recognize novel computer-made objects by performing an object discrimination task, in which an object had to be discriminated from others regardless of the change in viewing angle. Such object discrimination did not cause any significant change in either the averaged N1 amplitude or the averaged amplitude delay over the subjects. However, the dipole source analysis for N1 found statistically significant displacement of estimated dipole location caused by object discrimination training in the right hemisphere. The source obtained after training located significantly laterally than that obtained before training. The finding demonstrates the difference in neuronal representations of the experienced objects before and after view-invariant object discrimination learning, and suggests the different neuronal representations for object recognition with novel objects and familiar objects.
Patient transfer during nursing care imposes a heavy burden on long-term caregivers, frequently causing back pain. It is therefore necessary to improve nursing skills in order to reduce the burden on caregivers so as to prevent accidents. In this study, we fabricated capacitive body-pressure and proximity sensors that use conductive texture for detection, in order to evaluate nursing care movements. To investigate the response of the sensor, an experiment was conducted in which a stainless block was brought close to and then placed in contact with a simulated living body. The results show that the sensor can detect the distance to a living body, in addition to the applied pressure. Based on these response characteristics, a sitting experiment was conducted in which 10-channel body-pressure and proximity sensors were installed. The results indicated that the proposed sensor can be used to analyze the seating position of patients in a wheelchair based on the pressure on the seat surface.
In this study, we created a VR system using an HMD that allows nursing college students to simulate delirium, and clarified the physiological and psychological changes that occur when simulating delirium. The subjects were 18 males and 18 females, 6 from each of the grades 2 to 4 of the nursing school. The subjects were 18 males and 18 females, 6 from each grade of nursing school, from the second year to the fourth year. In this system, we created a VR video image to simulate postoperative delirium, which can be viewed on an HMD. Physiological data was obtained by showing the delirium video image after the subjects were sufficiently accustomed to viewing the VR. Autonomic functional activity (LF/HF) was measured in order to understand the stress level during video viewing. For psychological data, we used the Profile of Mood Scale (POMS2), a mood rating scale that can determine current mood before and after video viewing. The results of the analysis of the physiological data showed no significant differences. In the results of the analysis of psychological data, there were significant differences in all items. Based on these results, this study was judged to be useful as an experience for nursing students to attend to delirium patients.
The octave illusion occurs when two tones with one-octave differences are alternately played to both ears repeatedly. This study aims to classify participants into illusion and non-illusion groups by applying a convolutional neural network. Brain activity data were recorded using magnetoencephalography (MEG), and the activation levels between the two groups were analyzed. This study proposes a method for developing several layers of learning units to compare activities in the same brain region for the illusion and non-illusion groups. This study is one of the first attempts to apply deep neural networks for the classification of MEG data to illusion and non-illusion groups. The developed convolutional neural network showed stable results in the classification of octave illusion and non-illusion data with 100% accuracy and low training and validation losses, which indicate that no overfitting occurred. Furthermore, the pre-trained, octave illusion dataset convolutional neural network showed promising results in a similar auditory illusion data classification and can be used as a universal tool for classifying auditory illusions using MEG data.
The purpose of this study was to develop a computerized classification method for low-grade gliomas (LGGs) with/without 1p/19q codeletion on brain MRI using multi-scale 3D-convolutional neural networks (Multi-scale 3D-CNNs) with an attention mechanism that analyzes only the tumor region. Our database consisted of brain T2-weighted MRI images for 159 patients (102 LGGs with 1p/19q codeletion and 57 LGGs without it) from The Cancer Imaging Archive. The proposed method was constructed from a feature extractor, an attention mechanism, and a perception branch. In the feature extractor, the feature maps were extracted from input images. The attention mechanism generated the attention maps focusing on a tumor region from those feature maps. The feature maps on the tumor region were then obtained using an attention pooling with the attention maps. In the perception branch, the likelihood of LGG with 1p/19q codeletion was evaluated based on the feature maps of the tumor region. The classification accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve for the proposed method were 78.0%, 82.4%, 70.2%, and 0.838, showing a significant improvement when compared with the multi-scale 3D-CNNs without the attention mechanism (69.8%, 75.5%, 59.6%, and 0.717; p = 0.001).
Oral health is one of most major concerns that affect the life quality of billions of people around the world. Diagnosis treatment usually takes time due to the lack of doctors compared to a huge number of patients. Many researchers proposed methods to make an early disease detection for patients to assist doctors using computer aid diagnosis (CAD). However, most previous methods are not end-to-end methods and still require human involvement. The biggest challenge is that most researchers do not provide a good tooth detection technique before diagnosis. Therefore, the main objective, that builds a system to assist doctors, remains unaccomplished or just fairly successful. This paper proposed a detection method to localize the tooth using the Yolov3 model as a base network in the dental panoramic radiograph. The method consists of two main parts: image preprocessing and tooth localization. Firstly, because deep learning requires a big dataset, the original image is applied augmentation technique to improve the size of the dataset as well as diversity. Then, each image is resized to fit the input layer of the network; however, to prevent the information loss and boost the performance, we keep the original ratio of the images and change the ratio of the input layer in the model that can fit the image ratio. Next, we feed images into Yolov3, which is specially modified to fit the problem, for training. We add more detection heads into the backbone and concatenate the previous head detection's result with a proper layer to produce a more preeminent result. The final assessment shows an impressive result that the method reaches 95.58% and 94.90% for precision and recall, respectively. As a result, our proposed method is more reliable and practical in the tooth localization field, as well as helpful to reduce the doctor’s effort.
The purpose of this study was to assess the influence of ageing on reaching motion using a simple and quantitative method. An Electronic Evaluation System of Upper Extremity Function incorporating a motion sensor was used to monitor kinematic changes during a whack-a-mole type task. Sixteen healthy elderly adults (3 males and 13 females, mean age 78.8±6.0 years) and nineteen young adults (3 males and 16 females, mean age 22.1±2.8 years) participated in this study. The participants were asked to continue tapping 16 targets randomly presented on the screen. Based on the kinematic data, the tremor value, spatial travel distance, tapping time, and maximal spatial acceleration were calculated. Differences between elderly and young adults were tested using unpaired-t test. Significant differences were observed in all kinematic parameters, suggesting decreased reaching motion in elderly adults. Moreover, overall positive correlations between parameters were found. Our results suggest that kinematic parameters calculated from the motion sensor technology could be useful to evaluate reaching motion quantitatively and simply.
The rich dynamics of spatio-temporal activities in the neural system could be considered a computational resource. In this work, we attempted to quantify the capacity of information processing in the rat auditory cortex by applying a reservoir computing framework. Click-evoked multi-unit activities in the auditory cortex of rats were measured and considered as a reservoir, in which information processing capacity (IPC) was quantified. IPC was then compared with the performances of two benchmark tasks; shift register and logical calculation (AND, OR and XOR). Consequently, we found the first and second order IPC in the auditory cortex, which were positively correlated with the score of the benchmark tasks. These results suggest that IPC provides a new strategy to investigate sensory information processing in the brain.
The rich dynamics of spatio-temporal activity patterns in the brain might be exploited in the neuronal information processing. In dissociated cultures of neurons, we attempted to evaluate information processing capacity (IPC), a comprehensive measure of computational ability of input-driven dynamical systems. We found that dissociated cortical cultures had not only the 1st-order IPC, i.e., a linear memory of past inputs, but also the 2nd-order IPC, i.e., the product of past inputs. These IPC indicated that the dissociated culture was able to execute arithmetic operations and logical operations of past inputs by linearly reading out the state of spatio-temporal neural activities. These results indicate that a living neuronal culture can be considered and quantified as a computational resource.
It can be difficult for clinicians to correctly determine histological classifications of masses on breast ultrasonographic images. The purpose of this study was to develop a computerized classification method for histological classification of masses on breast ultrasonographic images using convolutional neural networks (CNN) with a ROI pooling that analyzes feature maps focusing on the mass region. Our dataset consisted of 585 breast ultrasonographic images obtained from 585 patients. It included 288 malignant masses (218 invasive and 70 noninvasive carcinomas) and 297 benign masses (115 cysts and 182 fibroadenomas). In this study, we developed a modified CNN model based on ResNet-18, in which the ROI pooling and two fully connected layers with a softmax function were introduced after the second and fourth residual block on ResNet-18, respectively. The proposed CNN model was employed to distinguish among four different types of histological classifications for masses. A three-fold cross validation method was used for training and testing the proposed CNN model. The average accuracy, sensitivity, specificity, positive predictive value and negative predictive value for the proposed CNN model were 81.7%, 91.0%, 91.2%, 91.0%, and 91.2%, respectively. Those results were substantially greater than those with ResNet-18 (70.3%, 83.0%, 87.2%, 86.3%, and 84.1%).
In recent years, non-invasive monitoring system for blood viscosity has been widely discussed. We have proposed a method to estimate the blood viscosity by measuring the degree of red blood cell aggregation with image correlation coefficient. In this paper, to estimate the aggregation degree in sample suspensions, histogram of the image correlation coefficient obtained from every correlation window on microscopic images was focused.
In order to provide products and services that are optimized to the values of each consumer, biometric information such as heart rate variability and electroencephalogram is useful for estimating values and preferences from the consumer’s unconscious. In this research, we create a heart rate variability spectrogram (HRVS) of a subject for a still image, and create a scalogram by using continuous wavelet transform in addition to the temporal differential change of the spectrogram. In addition, a scalogram is created using the continuous wavelet transform. From these, we consider whether human preferences can be discriminated using convolutional neural network (CNN) and support vector machine (SVM).
In recent years, Quality of Experience (QoE) from the user’s perspective, has been attracting attention. Attempts have been made to quantify QoE by clarifying changes in various biological information. In this study, we show the evaluation image that gives pleasant and unpleasant feelings to the subject, measure the changing cerebral hemodynamics by Near Infrared Spectroscopy (NIRS), and estimate the result by using wavelet transform and neural network. In addition, we propose a majority voting method, as learning of data from multiple Channel’s may be effective.
It has been reported that VR sickness can be suppressed by the placement of a fixation point in a field of view. However, the relationship between trajectory of the fixation point and VR sickness reduction effect is not made clear. Therefore, it is attempted that relationship is investigated using HMD in this study.
For realizing desired control performances, data-driven tunings such as virtual reference feedback tuning (VRFT) and fictious reference iterative tuning (FRIT) were proposed. However, these methods cannot realize desired control performances of autonomous vehicles because vehicle dynamics is time variant systems. To solve this problem, we propose gain-scheduled PI controls based on data-driven controls and model-based controls were proposed. The validity of proposed method is verified through vehicle simulators.
In this paper, we propose a method for indexing, analyzing, and evaluating of the stroking movement based on vibration features as a measurement method that mimics the response characteristics of finger associated with active touch. We developed the tactile information acquisition system to obtain the vibration information during stroking. Moreover, the verification experiment was performed using 13 types of cloth selected from the texture sample set. As the result, the two factors (roughness and hardness), which constitute the tactile sensation of the fabric, were extracted. It was also confirmed that the frequency bands of the vibration features extracted during active touch corresponded to the frequency sensitivity characteristics of the four sensory receptors inside the skin. Furthermore, we evaluated the predicted and measured values of each factor, and confirmed that the proposed method can construct a model that accurately predicts the tactile sensation by measuring the vibration of the interaction force on the contact surface. This method realizes the tactile evaluation for a various materials and physical quantities. Then, the method will contribute to the realization of quantitative evaluation of sensibility, which is the key technology to product design based on sensibility value.
Electromagnetic phantom is widely used to develop technologies based on the interaction between human and electromagnetic waves. In this paper, we evaluated the emulation performance of a multilayered HF-band phantom from the viewpoint of bioimpedance. The comparison among the measurements using multilayered/muscle phantom and the simulation using detailed arm model showed that the multilayered phantom could emulate the bioimpedance accurately.
In this paper, an adaptive algorithm of the dither gain based on the practical gradient method and its stability are considered. This adaptive algorithm is applied to the filtered-x active noise control system that is a typical example of the pre-inverse adaptive system and compared with the conventional algorithm. As a result, excellent tracking and steady-state characteristics were obtained.