Virtual reality (VR) has been applied to several fields such as entertainment, education, and medicine in recent years. VR is characterized by a high sense of immersion, which can be represented by the attention allocation from the real world to the virtual space. Although a high degree of attention allocation is significant in VR technology, most existing evaluation methods of VR applications are based on subjective questionnaires. Thus, quantitative and objective VR application evaluation methods are needed to realize advanced VR applications. In this study, we adopted a probe stimulus method to evaluate the attention allocation quantitatively and objectively in VR technology. Ten young adult participants underwent an auditory oddball task while they experienced VR content. The amount of attention directed to the VR content could be quantified based on the decrease in the event-related P300 wave response in the case of the oddball task. The participants watched two-dimensional and three-dimensional VR contents on a liquid crystal display and a head-mounted display, respectively, while brain activity was recorded in the form of electroencephalographic signals. A total of 230 probe stimuli at 1800 Hz (standard stimulus), 2000 Hz (target stimulus), and 500 Hz (deviant stimulus) were presented randomly via an earphone for 70 ms at 1000-ms intervals at the fractions of 70, 15, and 15%, respectively. Additionally, the reaction time and false reaction rate during the oddball task were measured as behavioral measures, and a questionnaire was used for subjective evaluation after the task. Based on a comparison of the subjective measure, behavioral measure, and amplitudes of P300 measured with the target stimulus from Pz and deviant stimulus from Cz, we found that attention allocation to the VR content can be quantitatively estimated using the amplitude of P300 for the deviant stimulus. These results suggest that the proposed method involving event-related potentials can be used as an indicator for attention allocation while watching VR content.

Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insignificant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work efficiency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subject's anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and specificity were used to evaluate the model, expressed as the percentage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subject's bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and specificity of 45.6 ± 46.7% and 83.88 ± 6.06%, respectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.

Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the preprocessing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient's posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same performance as the heuristically designed model.

Computer-aided diagnostic methods that provide semantic segmentation of texture patterns of diffuse lung diseases (DLDs) on chest computed tomography (CT) are extremely useful for detecting, identifying, and quantifying lung pathologies. While a fully annotated dataset is desirable to build a semantic segmentation model, building such a dataset for DLDs is costly due to the requirements of manual segmentation and certified experts for annotation. Partially supervised learning (PSL) has been proposed recently to take advantage of the partially annotated dataset and reduce the full annotation burden. Creating a partially annotated dataset is much less expensive than creating a fully annotated dataset. Therefore, PSL has great potential to build a semantic segmentation model that only requires a feasible amount of annotation. In this study, we propose a method of PSL employing a loss function that uses both annotated and unannotated pixels of a partially annotated dataset. The proposed loss function is based on the cross entropy loss, and it uses unannotated pixels to penalize the leakage of the segmentation. A parameter that controls the balance between the two types of supervision is introduced into the loss function to allow tuning and studying of the proposed PSL. The effectiveness and characteristics of PSL for the segmentation of DLD classes (consolidation, ground grass opacity, honeycombing, emphysema, and normal) were investigated in experiments using chest CT images of 372 patients. The experimental results show that the proposed PSL improved the mean Dice score from 0.76 to 0.79, and that a higher value of the balancing parameter increased the precision of the segmentation. Using the proposed PSL, which takes full advantage of the partially annotated dataset, we improved the accuracy of DLD segmentation. Furthermore, the experimental results clarified that the proposed PSL improved the precision of the models using unannotated pixels. Our implementation of the proposed PSL is available at https://ycdz-jsjc-gov-cn-s-1416.res.gxlib.org.cn:443/rwt/1416/https/M7VYI4DWMIYGG55N/yk-szk/psl-dld.

Appropriate evaluation of the intraoperative state of a surgical team is essential for the improvement of teamwork and hence a safe surgical environment. Traditional methods to evaluate intraoperative team states such as interview and self-check questionnaire on each surgical team member often require human efforts, which are time-consuming and can be biased by individual recall. One effective solution is to analyze the surgical video and track the important team activities, such as whether the members are complying with the surgical procedure or are being distracted by unexpected events. However, due to the complexity of the situations in an operating room, identifying the team activities without any human effort remains challenging. In this work, we propose a novel approach that automatically recognizes and quantifies intraoperative activities from surgery videos. As a first step, we focus on recognizing two activities that especially involve multiple individuals: (a) passing of clean-packaged surgery instruments which is a representative interaction between the surgical technologists such as the circulating nurse and scrub nurse, and (b) group attention that may be attracted by unexpected events. We record surgical videos as input, and apply pose estimation and particle filters to extract individual's face orientation, body orientation, and arm raise. These results coupled with individual IDs are then sent to an estimation model that provides the probability of each target activity. Simultaneously, a person model is generated and bound to each individual, which describes all the involved activities along the timeline. We tested our method using videos of simulated activities. The results showed that the system was able to recognize instrument passing and group attention with F1 = 0.95 and F1 = 0.66, respectively. We also implemented a system with an interface that automatically annotated intraoperative activities along the video timeline, and invited feedback from surgical technologists. The results suggest that the quantified and visualized activities can help improve understanding of the intraoperative state of the surgical team.

In addition to traditional clinical research, advances in information communication technologies facilitates new medical research using internet of things devices and other cutting-edge technologies. Such medical research also simplifies the collection of data on research subjects in their daily lives internationally. In this context, medical research is increasingly required to comply with rules protecting patients' personal data. This study proposes a model to enable researchers and other stakeholders including ethics committees in such international medical research to easily verify whether the planned processing of patient data complies with relevant legal and ethical rules. The model proposed in this study consists of (1) how patient information is processed, (2) the rules that are relevant to the processing, and (3) the analysis of whether the processing complies with the rules. This study suggests that the model should describe the aspects of data processing that are subject to many rules, such as the location of the processing, categories of data, purposes of the processing, and the storage period. Thus, using the information described in the model as a guide, stakeholders can determine which national and international legal/ethical rules apply to the planned processing. Then, they can use the model to verify and document whether the processing complies with the specific regulatory rules. The use of the model in this study enables stakeholders in medical research to comply with the rules related to patient data more effectively than without using the model.

Sudden deterioration of condition in patients with various diseases, such as cardiopulmonary arrest, may result in poor outcome even after resuscitation. Early detection of deterioration is important in medical and long-term care settings, regardless of the acute or chronic phase of disease. Early detection and appropriate interventions are essential before resuscitating measures are required. Among the vital signs that indicate the general condition of a patient, respiratory rate has a greater ability to predict serious events such as thromboembolism and sepsis than heart rate and blood pressure, even in early stages. Despite its importance, however, respiratory rate is frequently overlooked and not measured, making it a neglected vital sign. To facilitate the measurement of respiratory rate, a non-invasive method of detecting respiratory sounds was developed based on deep learning technology, using a built-in microphone in a smartphone. Smartphones attached to the bed headboards of 20 participants undergoing polysomnography (PSG) at Kyoto University Hospital recorded respiratory sounds. Sound data were synchronized with overnight respiratory information. After excluding periods of abnormal breathing on the PSG report, sound data were processed for each 1-minute period. Expiration sound was determined using the pressure flow sensor signal on PSG. Finally, a model to identify the expiration section from the sound information was created using a deep learning algorithm from the convolutional Long Short Term Memory network. The accuracy of the learning model in identifying the expiratory section was 0.791, indicating that respiratory rate can be determined using the microphone in a smartphone. By collecting data from more patients and improving the accuracy of this method, respiratory rates could be more easily monitored in all situations, both inside and outside the hospital.

A conventional electrolarynx (EL), which is used by laryngectomees, produces monotonous sound and occupies a user's hand; hence, we developed a hands-free wearable device that improves voice quality. The proposed device estimates individual vocal tract features using linear predictive coding (LPC) and generates sound vibrations using an LPC inverse filter. Additionally, we reproduced the vibration sound using a transducer and amplified the first harmonic frequency and the second one. We conducted an objective experiment to compare the spectra of natural voice, a conventional EL, and the proposed device. We also conducted a subjective experiment in which we asked healthy subjects to listen to and evaluate the conventional EL and the proposed device. The results of the objective experiment demonstrated that our model was characterized by two formant peaks that were similar to the conventional EL and the natural voice. The results of the subjective experiment demonstrated that our model was more powerful and clearer than the conventional EL. These findings indicate that the voice of our device is spectrally close to human voice and gives the audience a more powerful and clearer sound.

Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs.

Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images.

Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively.

Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies.

Laparoscopic surgery holds great promise in medicine but remains challenging for surgeons because it is difficult to perceive depth while suturing. In addition to binocular parallax, such as three-dimensional vision, shadow is essential for depth perception. This paper presents an augmented reality system that draws virtual shadows to aid depth perception. On the visual display, the system generates shadows that mimic actual shadows by estimating shadow positions using image processing. The distance and angle between the forceps tip and the surface were estimated to evaluate the accuracy of the system. To validate the usefulness of this system in surgical applications, novices performed suturing tasks with and without the augmented reality system. The system error and delay were sufficiently small, and the generated shadows were similar to actual shadows. Furthermore, the suturing error decreased significantly when the augmented reality system was used. The shadow-drawing system developed in this study may help surgeons perceive depth during laparoscopic surgery.

Deterioration of the skin barrier function causes symptoms such as allergies because various chemical substances may enter the human body. Quantitative evaluation of the thickness and water content of the stratum corneum is useful as a measure of the skin barrier function in domains such as dermatology, nursing science, and cosmetics development. The stratum corneum is responsible for most of the skin barrier function, and two factors—the thickness and water content of the stratum corneum—are thus important. In this paper, the stratum corneum is regarded as a parallel model of resistance and capacitance. From measurements of the electrical impedance of the skin, we propose a new model for simultaneous estimation of the thickness and water content of the stratum corneum conventionally measured by a confocal laser scanning microscope and a confocal Raman spectrometer, respectively, and we discuss the results of the measurements. The electrical impedance of the skin was measured using a device that we developed. The measurement began 3 seconds after the electrodes on the measurement head of the device came into contact with the skin, and parameters including the impedance, which was obtained by applying an alternating current signal at two frequencies, were measured. We measured the thickness and water content of the stratum corneum using confocal laser microscopy and confocal Raman spectroscopy, respectively; investigated the relationship of the thickness and water content of the stratum corneum with the electrical impedance of the skin; and established a new potential model for estimating the thickness and water content of the stratum corneum from the parallel resistance and capacitance. The correlation coefficients of the verification data were 0.931 and 0.776, respectively; and the root-mean-squared error of the thickness of the stratum corneum was 2.3 µm, while the root-mean-squared error of the water content at the surface of the stratum corneum was 5.4 points. These findings indicate the feasibility of quantitative evaluation of the thickness and water content of the stratum corneum by measuring skin electrical impedance.

Numerous studies have suggested that sleep spindle waves may play a role in the hippocampal-cortical transmission of information associated with memory enhancement. In previous research, the clustering coefficient increased significantly from wakefulness to sleep, indicating that the graph theory may be able to characterize brain network activity during sleep. However, previous studies have not investigated in detail the characteristics of the brain network in individual sleep stages; the brain network activity in the EEG at each sleep stage has not yet been clarified. In this study, we compared the characteristics of the network activity in various sleep stages by determining the functional connectivity from EEG in individual stages, constructing the networks and comparing the clustering coefficients and characteristic path lengths. We found a significant decrease in the characteristic path length in LowBeta band (13–15 Hz) from Stage 1 to later stages. However, there was no significant difference in the clustering coefficient. Our results are consistent with the concept that sleep spindles are related to memory consolidation. Therefore, the results suggest that the networks generated by the brain are more efficient in middle and deep sleep.

Ventricular fibrillation (VF) causes failure of synchronous contraction of the heart ventricles, resulting in cardiac collapse. Currently, VF is still the major cause of sudden cardiac death, and strategies such as prevention, prediction, and immediate termination are not yet established. This article reviews the progress of research on VF mechanism and proposes a future direction to elucidate it. Historical hypotheses proposed for VF mechanism were wandering wavelets, mother rotors, and multiple foci. Current concept is a combination of these hypotheses, but remains to be explicated. Rotor, a spiral shape of the excitation wavefront, plays a major role in VF. The mother rotor eventually breaks up and creates multiple unstable rotors by pathological and electrophysiological abnormalities, terminating as irreversible VF. The dynamics of rotors are influenced by many factors such as ion channel modification, alternance of intracellular calcium, and restitution characteristics of the repolarization duration. The break-up of a rotor is created by head–tail interaction of the rotor, or the area of the ischemic tissue zone. The advance of computer approach has realized simulation of the complex heart model, which provides deep insight into electrophysiological background of VF in three dimensional settings. Also, many mapping techniques including not only activation and repolarization mapping, but also phase variance analysis help understanding the rotor/filament mechanism during VF. Topological analysis and chaotic approach have been developed to evaluate the mechanism of VF, but it is still impossible to control the chaotic behavior of VF. Many cardiac and non-cardiac factors induce or reduce the VF characteristics. Cardiac factors include Purkinje fiber, intracellular calcium dynamics, ion channels, and gap junction. Interventions of these parameters have the potential to prevent or induce VF. Optimal control of cardiac factor(s) may be used as VF control therapy, but a clinically useful method is yet to be developed. Non-cardiac factors influencing VF include hypokalemia, hypothermia, heart failure, cardiac ischemia, and autonomic nerve. The detailed mechanisms of VF modification for each factor have been clarified; however, no universal mechanism related to VF is established. The mechanism of VF remains to be determined in order to accomplish VF therapy.

The production of disposable diapers is rising due to an increase in number of elderly people who require nursing care. When providing diaper care, it is difficult to know remotely the time of urination and amount of urine absorbed by a diaper. Hence, a remote sensor system that automatically records the time and amount of urine in a diaper is being developed. In this study, a digital capacitive sensor system was developed to estimate the amount of diaper absorption. Preliminary and phantom experiments were conducted to investigate the basic performance of the developed system. We also measured the amount of urination of two subjects using the developed system. A phantom, a waist-type torso mannequin filled with saline solution, and two male subjects wore pad-type diaper on the inside and tape-type diaper on the outside. Electrodes were attached to the outer surface of the tape-type diaper to measure the change in capacitance caused by urination. In the preliminary experiment, the developed system measured capacitors with known capacitance within 5.47% error. In the phantom experiment, the change in capacitance decreased as the amount of pseudo-urine absorbed by the pad-type diaper increased. Moreover, the results of human subjects urinating five times were consistent with the results of the preliminary experiment. Therefore, remote and quantitative evaluation of the urine absorption volume was possible using the developed digital system.

Non-invasive magnetic stimulation has the potential to be an effective treatment for Alzheimer's disease, which inevitably involves neurodegeneration of the hippocampus at the base of the brain. In this study, we fabricated an excitation coil designed to be mounted inside the oral cavity, i.e., an intraoral coil, to stimulate the base of the brain. The oral cavity has an advantage of being in closer proximity with greater transitability to the base compared with the convexity of the brain. We determined the optimum angle of the intraoral coil targeting the base to achieve the largest magnetic flux density. In the experiments, the coil angle was changed and the z-component of the magnetic flux density was measured at positions corresponding to three regions at the base of the brain: hippocampus, thalamus, and hypothalamus. Here, a single pulse current of approximately 1920 A at 3 kHz was applied to the intraoral coil with a power supply device used in actual transcranial magnetic stimulation (TMS). A maximum magnetic flux density of 10.3–17.2 × 10⁻³ T was obtained at the base of the brain when the coil angle was between 35 and 40 degrees. Next, we conducted a numerical analysis using a numerical human head model. The z-component of the maximum magnetic flux density was obtained at coil angles between 35 and 40 degrees, and the result was consistent with the experimental result. Additionally, we evaluated the induced current density at the base of the brain by numerical analysis. The maximum value at the hippocampus was 2 A/m² when the coil angle was 90 degrees. This is the angle at which the central axis of the designed intraoral coil (a solenoid coil) is horizontal to the ground. Specifically, the central axis of the present intraoral coil needs to be tilted to 60 degrees from the target.

Cardiac arrest has been reported during participation in several sports. Of these sports, marathon running is a particularly popular sport but imposes high cardiac load. Indeed, its popularity has been growing worldwide. Risk of cardiac arrest during marathon races is also expected to increase. Several studies have recorded electrocardiographic (ECG) information during marathon races to protect athletes from cardiac arrest. Although evaluable ECG data have been obtained and analyzed, cost-effectiveness of the system, data quality, and clinical significance remain inadequate. This report is the first to describe an economical electrocardiograph built into a T-shirt for use during marathon race. Twenty healthy runners aged 20 to 59 years (mean 36 years) wore the ECG device while running. The ECG data were monitored and analyzed to assess the observed frequencies of specified arrhythmias and the sections of the marathon in which the arrhythmias occurred. Of the ECG data obtained from 14 runners who completed the full marathon, six ECG datasets were evaluable. In some runners, there was inadequate contact between the electrode and body surface or poor Bluetooth connection between the ECG wireless transmitter and smartphone. Regarding arrhythmia analysis, all evaluable data that were analyzed showed some rhythm fluctuations. In conclusion, this economical T-shirt type ECG sensor provided evaluable ECG data during marathon races, although the evaluable rate was not high. The data were used to analyze specified arrhythmias, but some difficulties were encountered. The ECG sensor did not function properly because of a system error. The ECG sensor was not adequately moistened to record ECGs accurately. Moreover, some runners chose an unsuitable shirt size, which impaired the stability and strength of the electrode–skin contact. These shortcomings produced noise in the ECG data, which made it difficult to analyze arrhythmias. The next step will be to solve these problems and acquire data from a large number of runners.

Atrial fibrillation (AF) is the leading cause of cardiogenic cerebral embolism (CE), which is the most severe course of ischemic stroke. AF detection is important for preventing the onset of CE, but it is not always easy to diagnose AF before the onset of CE because almost half of the patients with AF are asymptomatic. Here, we report the development of an extremely simple yet highly accurate AF screening system that can measure pulse simply by placing the palm on the detector. Videoplethysmography (VPG) was used in the pulse-measuring device. This system consists of a Windows personal computer (PC) connected to a web camera, and the PC is equipped with a non-contact vital sensing software “Rhythmiru” and a pulse wave analysis program. Pulse wave information was acquired by analyzing video images of the palm. The pulse data were evaluated in two stages using power spectrum analysis and coefficient of variation analysis, based on which AF was determined. The measurement time was less than 1 min, which is satisfactory for practical use. Electrocardiography was performed simultaneously with pulse measurement using this device to evaluate the accuracy of the system. Among 128 patients, 116 were analyzed after excluding duplicate patients and those who had technical problems. The 116 patients included 28 with AF and 88 without AF. The system had 96.4% sensitivity and 97.7% specificity for AF detection. Our method of assessing pulse variability using the palm surface based on VPG information was feasible and highly accurate. AF detection efforts can lead to early detection of asymptomatic AF, resulting in a reduction in CE.

Deformities of the foot result in loss of walking function. The skeletal structure of the foot develops during childhood, and quantitative assessment of the foot is warranted. In healthy children, foot length and navicular height have been studied individually; however, the foot has not been sufficiently studied in its entirety, and the growth curve of the foot has not been clarified. In particular, the heel is an important site influencing foot biomechanics. In this study, we developed a three-dimensional (3D) foot scanner and digital footprint device to examine the foot characteristics of both children and adults. This study aimed to compare the foot characteristics of developing children with those of adults. Overall, 154 children (aged 8–10 years) and 403 adults (aged 40–89 years) were included in the study. The 3D foot scanner and digital footprint device were used for measurements. Foot length, forefoot width, foot height, navicular height, and heel width were evaluated. In particular, the ratio of each indicator to foot length was used to evaluate the characteristics of the child's foot. The measurement indices were significantly larger in adults than in children, indicating that the indices increase as children grow, irrespective of sex. The results of the measurements and the ratios to foot length showed large individual differences between adults and children in some of the measurement indices. However, there was no difference in the heel width between children and adults. In contrast, forefoot flattening was greater in adults. Increases in foot length and heel width were found to vary greatly among individuals and those with different characteristics. While foot length and navicular height increased in adults, heel characteristics did not differ significantly between children aged 8–10 years and adults. Children appear to show foot geometries and proportions different from adults, which would have important implications for the ergonomics of children's footwear.

Optic flow (OF) stimulation, which uses virtual reality (VR) technology to present images that induce motor vision different from the actual walking speed during treadmill walking, is expected to be a rehabilitation technique that improves the gait function in stroke patients. This study aimed to investigate the effects of VR treadmill gait training under various OF speeds in stroke patients and healthy controls. Twelve healthy young adults and 10 stroke patients walked on a treadmill with speed set at their comfortable walking speed for 5 min each under four conditions: no OF stimulation and OF stimulations at 100%, 125%, and 75% of the treadmill speed. We evaluated the changes in walking time, the number of steps, and walking rate (steps/min) in a 10-m walking test performed before, immediately after, and 5, 10, and 20 min after treadmill training. A robust facilitating effect on gait function was observed in treadmill training with 125% OF stimulation speed. In stroke patients, the walking time and number of steps were significantly reduced relative to the pre-training state from immediately to 10 min after treadmill training at 125% OF stimulation speed. The walking rate also increased significantly 10 min after training. In healthy controls, the walking time decreased significantly from immediately to 10 min after training at 125% OF stimulation speed compared with the pre-training state. The number of steps also decreased significantly, but only immediately after the training, while the walking rate did not change throughout the experiment. In conclusion, OF stimulation at a speed faster than the actual locomotion speed effectively increases the walking speed, and this effect persisted for approximately 10 min after training in both stroke patients and healthy participants who used different neural strategies to enhance gait function.

Data compatibility in Electronic Medical Records (EMR) among healthcare facilities is necessary, especially for medical practitioners such as doctors or physicians, so that they can grant a more accurate decision on what treatments should be carried out for their patients, since a precise treatment or medication will increase the chance that patients would successfully heal from their disease. The compatibility of EMR data can also be called interoperability. This research attempts to apply interoperability of healthcare data by implementing an automatic mapper of an EMR data from one EMR management system called OpenEMR so that its data can meet the FHIR (Fast Healthcare Interoperability Resources) standard. Specifically, a classifier to categorize the OpenEMR data into the appropriate FHIR format is discussed in this paper. There are three classifiers developed in Java and Python, which utilize the concepts of machine learning classification techniques; in this case, Naïve-Bayes and Decision Tree. Implementations of both machine learning algorithms showed a classification accuracy of 100%, which resulted in the additional implementation of rule-based technique, which also resulted in 100% accuracy. After running similar tests on all three implementations, the results infer that the rule-based technique is better than Naïve-Bayes for development in Java programming language.

Implantable ventricular assist devices (iVADs) are used for severe heart failure therapy. A flow rate estimation method is required for adequate control of the blood pump flow rate without using a flow meter in iVAD therapy. We developed a flow rate estimation method using the eccentric position of a magnetically levitated (maglev) impeller determined by radial passive stability. This estimation method meets clinical requirements because of high estimation accuracy even under varying blood viscosity (pump Reynolds number, Re_p). In this study, a computational fluid dynamics (CFD) analysis was performed to clarify the basis of the maglev impeller passive stability, aiming at clinical application of this method. First, no significant variation in the pump pressure distribution, which determined the impeller's passively stabilized position, was observed under typical blood pump operating conditions (Re_p = 35000–63000). In contrast, the working fluid density change affected the estimation accuracy by changing the pump characteristics. However, its effect on the estimation accuracy was less than 1% because the blood density can only change within a narrow range. These results indicate that the estimation method can be applied to other devices with a passively stabilized impeller. Additionally, CFD analysis indicates that the variation in the pump flow path design based on radial clearance can change the impeller radial position, the radial hydraulic force exerted on the impeller, and its magnitude with respect to the pump flow rate. The CFD results suggest that the resolution of the method can be adjusted by changing the flow path design of the radial clearance. However, the radial hydraulic fluid direction was almost constant in the radial clearance range of 1.0–3.0 mm. Therefore, the variation in the impeller radial position direction with respect to the pump flow rate did not change significantly with the radial clearance design under the present analysis conditions. However, the rate of variation in the impeller radial position with respect to the pump flow rate, which determines the accuracy of the estimation method, can be adjusted with the radial clearance design. The estimation method, which has remarkable characteristics, evaluated in this research has the potential for use in clinical practice.

The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a comprehensive database containing health insurance claim information. The structure of the NDB complicates long-term cohorts for two main reasons. First, the NDB data are stored on a per-claim basis. Second, the NDB is a billing-focused record structure. Therefore, the objective of this study was to use ID0 to modify the data structure to allow for long-term cohorts, provided that the data volume is not increased and the runtime per data year is maintained within one month. The NDB uses two primary keys (ID1 and ID2) made from hash values that mask personally identifiable information. ID0 is our recently developed key from ID1 and ID2, which improves patient-matching efficiency with excellent long-term tracing performance. Our study used claim data with filing dates between April 2013 and March 2016 to trace hospitalizations of one month or longer, including outpatient care, in three steps. In Step 1, claims were transferred to a CD-record format. As some diagnosis procedure combination (DPC) claim records contain a mixture of overlapping comprehensive and piece-rate data, we sorted and reorganized them. In Step 2, pharmacy and medical outpatient claims were integrated using the ID0 key, the medical institution code for issuing a prescription, and the prescription issue date. In Step 3, the transferred data were combined and converted from consecutive hospitalization days into sequences based on ID0, the medical institution code, and hospital ward classification. Consequently, the size of the originally extracted comma-separated variable dataset for three years (approximately 10.5 TB) was reduced to an approximately 6 TB main database file that was usable for processing. The process took approximately three months. With similar conventional methods, the data size was 30 times larger, and it took more than seven months to process a year's worth of data. In addition, to demonstrate the application of this method, we conducted a six-year mortality cohort for all Japanese citizens. Our technique makes it easy to perform follow-up and longitudinal cohort surveys while accurately tracing patient data in large-scale medical claims databases.

Evaluating patients with paralysis of the lower extremities resulting from diseases of the central nervous system is essential for planning appropriate treatments and evaluating their effects. For patients with paralysis, previous studies have proposed that the dynamic properties of the knee joint be evaluated based on the pendulum motion of the lower leg. However, objective measurement of joint spasticity is challenging, and previous studies have utilized a variety of indicators, making direct comparison difficult. This study aimed to develop a new method for estimating and analyzing pendulum motion based on three dynamic properties (moment of inertia, viscosity, and stiffness). Thirty-two individuals (20 men and 12 women; mean age: 69.5 ± 10.5 years) who developed spasticity in the quadriceps femoris muscle on the paralyzed side ≥ 1 month after stroke onset were included, along with 20 healthy community-dwelling individuals (10 men and 10 women; mean age: 68.4 ± 6.9 years). For the pendulum test, the end block of a goniometer was attached to the outer side of the distal thigh and lower leg of the target limb to interpose the knee joint. The malleolus lateralis and malleolus medialis of the target limb were grasped and allowed to fall from a 45° flexed position, following which the knee angles were measured. Independent component analysis (ICA) was used to obtain pure pendulum motion data without the influence of measurement errors caused by the initial angle of the knee joint and the influence of trunk posture. This study is the first to quantify knee joint viscosity and stiffness as parameters of a linear model and goodness-of-fit as a parameter representing a deviation from the linear model by applying ICA and a genetic algorithm. Because the values of various parameters affecting pendulum motion can be determined using our model, it is possible to simulate the effects of physical therapy on gait under various conditions (e.g., rehabilitation interventions, use of orthotic devices). Our objective and quantitative method, which is simple and does not require consideration of measurement error during pendulum testing, may also aid in elucidating the mechanisms underlying the development of spasticity.

Intestinal obstruction is generally treated with an ileus tube. Success of treating functional ileus due to abnormal intestinal motility is judged through auscultation and medical interviews. Therefore, functional tests to visually evaluate the motility of the small intestine are important to elucidate the pathophysiology and observe the recovery process. An internal pressure measurement method that directly measures small intestine motility has been explored as a type of functional test. However, because the catheter is not in contact with the inner intestine wall, it is possible to measure the peristalsis movement, which is a large motility, but not the automatic rhythmic movement, which is a small motility. Therefore, we have developed a small intestine motility measurement system using an ileus tube. Small intestine motility is measured using the pressure applied to a balloon attached to an ileus tube. Peristaltic and automatic movement can be measured as the balloon contacts the inner intestinal wall, allowing more detailed evaluation of small intestinal motility. This study aimed at determining whether this measurement system can be applied clinically to accurately measure the small intestine motility. Hence, we first applied pressure to the balloon of the ileus tube using a pressurizing device to evaluate the basic performance ex vivo. We then conducted preliminary tests on a healthy volunteer. Analysis of the pressure test results showed that although the time delay of the balloon pressure measurement with respect to pressurization status was 4–6%, the cycles of the balloon pressure and displacement were consistent. Thus, it was concluded that the time delay had no effect on the proposed small intestine motility measurement system. In the preliminary test in a healthy volunteer, the pressure waveforms obtained during testing revealed a mixture of large and small pressure values of ~2 kPa at ~1–2 cycles/min and ~0.5 kPa at ~6–11 cycles/min, respectively. We confirmed that the larger pressure was similar to a peristalsis motility pattern, whereas the smaller pressure was similar to an automatic motility pattern. These results demonstrate that our measurement system is able to measure the small intestine motility and thus can be applied to comprehensive and visual measurement of small intestine motility.

Advanced technologies in bioinstrumentation allows easy monitoring of biometric signals such as electrocardiogram (ECG) and respiration. In order to improve unreliable monitoring due to missing RR intervals (RRIs), this paper proposes a missing RRI complement method based on respiratory features. The proposed method first selects respiratory features from the measured data based on Granger causality, and then complements the missing RRIs based on a dynamic linear model (DLM) for RRIs with selected features. The performance of the proposed method was evaluated by comparison with standard spline interpolation, standard regression, and a vector autoregressive (VAR) model. The results are discussed in terms of the effectiveness of respiratory feature selection and utilization of the DLM to capture temporal fluctuations.