Deterioration of skin barrier function causes symptoms such as allergies because it allows various chemical substances to enter the human body. Quantitative evaluation of the thickness and water content of the stratum corneum is useful as a measure of skin barrier function in fields such as dermatology, nursing science, and cosmetics development. The stratum corneum is responsible for most of the skin barrier function, and this function has conventionally been evaluated using transepidermal water loss (TEWL). In this paper, we propose a new model for estimation of TEWL from measurements of the thickness of the stratum corneum and water content of the surface of the stratum corneum, and discuss the results of the measurements. By measuring the thickness and water content of the stratum corneum using confocal laser microscopy and confocal Raman spectroscopy, respectively, and examining the relationship of these variables with TEWL, we established a new potential model for estimating TEWL from these two variables. The correlation coefficient of the validation data was 0.886 and the root mean squared error was 8.18 points. These findings indicate the feasibility of qualitative evaluation of TEWL by measuring the thickness and water content of the stratum corneum.

This study examined whether six dispositional psychological measures; Frequency of Flow Experience, Resilience, Self-Esteem, Self-Efficacy, Will for Meaningful Life, and Trait-Anxiety are associated with heart rate diurnal rhythm parameters. The associations of four physiological parameters—24-hour and 12-hour periodic component amplitudes, diurnal heart rate range amplitude, and autonomic switching rate—with the six dispositional psychological measures were analyzed. The physiological parameters were extracted using two different methods from heart rate data continuously recorded at one-minute intervals by a wrist device. Conventional cosinor and spline-based methods were used. The study was conducted on 20 healthy individuals aged 25–57 years. Single regression analysis showed a significant correlation of Frequency of Flow Experience, Resilience, and Will for Meaningful Life with heart rate rhythm parameters (p < 0.05), and a trend of significant correlation of Self-Esteem with heart rate rhythm parameters (p < 0.1). On the other hand, Self-Efficacy consistently showed a positive correlation, while the only negative psychological measure; Trait-Anxiety, showed a negative correlation with heart rate rhythm parameters, although statistical significance was not reached (p > 0.1). Principal component analysis extracted two orthogonal components with which multiple principal component regression yielded better R² (coefficient of determination) values than single regression for Frequency of Flow Experience (R² = 0.451, p < 0.05), Resilience (R² = 0.587, p < 0.05), Self-Esteem (R² = 0.494, p < 0.05), Will for Meaningful Life (R² = 0.364, p < 0.05), Self-Efficacy (R² = 0.322, p < 0.1), and Trait-Anxiety (R² = 0.241, p > 0.1). Based on these results, positive dispositional psychological measures were associated with physiological parameters representing long-term characteristics of autonomic nervous activity. The research outcome may be applied to develop a ubiquitous healthcare monitoring system that integrates both physiological bio-signals and psychological measures.

The anatomical features of human organs inherent in 3D medical images provide essential information for diagnosis, pre-treatment planning, and surgical guidance. However, the image quality, dimensions, and field of view of medical images can be restricted by the physical or hardware limitations of the imaging devices available during treatment. Medical image synthesis is a research field with a wide range of potential applications in radiology and computer-assisted surgery, and is of growing interest in a variety of medical imaging modalities. This paper reviews recent studies on medical image synthesis and statistical reconstruction of anatomical information not included in original images, specifically focusing on the two topics: 1) recovery of pixels missing due to image artifacts, and 2) higher-dimensional image reconstruction or so called 2D-to-3D (2D/3D) techniques

In this study, we focused on the fluctuation of the abdominal skin temperature (AST) during sleep as a second marker for determining the biphasic menstrual cycle, alongside the basal body temperature. The nocturnal AST was measured every 10 min using a wearable device mounted on the abdominal wall. With this system, the AST time-series data were recorded for a total of 1667, 1035, and 1690 days from seven participants for the menstrual/follicular, ovulatory, and luteal phases, respectively. First, the AST fluctuation was evaluated by plotting the cumulative probability distribution (CPD) of changes in AST every 10 min from 0 to 0.7℃. The results showed that the CPD fitted well with an exponential attenuation curve. Second, the mean attenuation coefficients obtained by exponential regression from the CPD data were compared among the three phases. For regular menstrual cycles, the attenuation coefficient was the highest in the menstrual/follicular phase (8.57; 95% confidence interval 8.44–8.70; R² = 0.983; P < 0.001), followed by the ovulatory phase (7.80; 95% confidence interval 7.65–7.96; R² = 0.985; P < 0.001) and then the luteal phase (7.24; 95% confidence interval 7.12–7.36; R² = 0.985; P < 0.001). Finally, we examined whether the attenuation coefficients can be used as an index to classify the three phases by long short-term memory (LSTM)-based deep learning. Consequently, the attenuation coefficient affected the prediction of the menstrual/follicular, ovulatory, and luteal phases with significantly higher F-measures of 0.603, 0.328, and 0.660, respectively. These results suggest that the thermoregulatory system may increase the AST fluctuation in healthy women during the transition from the follicular phase to the ovulatory phase and then to the luteal phase.

A visual search is implemented when the eye moves to find a target symbol amongst many other symbols (distractors). The efficiency of a visual search is described by Hick's law, which shows that the search time increases logarithmically as the number of symbols increases. In this paper, the efficiency of visual search was analyzed from the perspective of the network features of a conceptual ‘unobservable’ gaze position network superimposed on a monitor screen filled with many symbols (search array board). We assume that the gaze position does not move freely around the search array board, but rather moves in a way restricted to the unobservable gaze position network. First, we statistically verified that the artificial gaze position network designed from the data of visual search experiments have small-world features, and depends on the ratio of the saccades. Second, by implementing gaze step simulations on such small-world networks, we statistically verified that the simulation search times were close to those obtained from the experiments and also to the minimum search times. Thus, this study suggests that an efficient visual search can be explained by a small-world architecture hidden in the unobservable gaze position network and thus has to be artificially designed.

In this study, we performed simulations for bone scintigrams before and after a hot spot detection support system was fine-tuned using a postmarket dataset, and statistically identified the factors that affect the changes in performance. Datasets from five hospitals were used to train the premarket system, and the dataset from another hospital was added to fine-tune the system. We applied the premarket and postmarket fine-tuned systems to postmarket test data and computed the difference in the number of pixels of false positives and false negatives before and after fine-tuning. Structural equation modeling was used to analyze the relationship between the four possible factors and performance changes. The experimental results indicated that the image contrast and number of pixels of hot spots per image were the main factors affecting the performance. In addition, we identified the conditions for determining whether fine-tuning the system using postmarket datasets is appropriate. The experimental findings from this study will be useful for deriving an effective design scheme for continuous learning in artificial intelligence systems.

In this study, we developed a new electronic musical instrument, Cymis, with which notes are played by touching a touch screen and the volume of the note is controlled by expiratory pressure. It can be played with about the same amount of exhalation as a keyboard harmonica. Performance experiments were conducted with healthy adults (11 persons) in their 20s. First, all were able to play solo using Cymis. Next, simulated ensemble performances were conducted. Using a system that can predict the ideal timing for sounding notes from visual information, six novice performers performed a piece (Amazing Grace) after having had from one hour to several weeks of practice. For each note in the ensemble performance, the difference between the actual and ideal times at which the note was produced, td, was measured. In the experiments, |td| was found to be less than 200 ms for more than 85% of the total number of notes played. In an ensemble performance in which five experienced performers listened to the sound of the performance, this result was more than 80%. This study shows that a novice performer using Cymis can play in an ensemble with about the same degree of temporal accuracy as an experienced performer.

Obstructive sleep apnea syndrome (OSAS) is a disorder that causes sleep apnea and hypopnea, which in turn causes various disorders in daily life. Because of the difficulty in measuring airflow dynamics, computational fluid dynamics (CFD) simulations are performed to evaluate upper airway airflow in OSAS in detail. However, the relationship between the severity of OSAS, as measured by the apnea hypopnea index (AHI), and airflow dynamics is unclear. In this study, CFD simulations of human snoring during sleep were performed to determine the correlation between AHI and pressure drops in the nasal cavity and throat, as well as between AHI and minimum cross-sectional area of the throat. For the simulation, 3D models of snoring in the open-mouth state, which is a common form of snoring, were reconstructed based on computed tomography images acquired from four patients with mild OSAS and six with severe OSAS. Each relationship was evaluated using Spearman's rank correlation coefficient. The correlation coefficient between AHI and pressure drop in the nasal cavity was 0.745, with a significant correlation. There was no significant correlation between AHI and pressure drop in the throat or between AHI and minimum cross-sectional area of the throat. These results suggest that the pressure drop in the nasal cavity affects the severity of OSAS.

Fine motor dysfunction and cognitive impairments commonly develop after stroke, which greatly impact the daily lives of patients. In current occupational therapy, hand dexterity and cognitive functions are evaluated individually (e.g., by manipulation of small objects with fingers, or a paper-and-pencil test), which is insufficient for therapists to grasp the total ability of combined dexterity and cognition in everyday situations. Additionally, the traditional methods require a tester to measure the completion time manually and tend to be monotonous for patients. These problems would be solved using technology. This study aimed to develop a new electric pegboard (e-Peg) prototype and to investigate preliminary utility in healthy adults. The system judges the peg insertion accuracy based on magnetism and records the time course and scores, which are linked to human object manipulation ability. The e-Peg executes three types of tasks: a basic color matching task (BT), a color comparison task using a pattern sheet (CT), and a visual memory task (MT), with one/two-color sample patterns. Six older and nine younger healthy adults performed the e-Peg tasks, functional tests, and responded to questionnaires. As a result, the number of correct answers in a bicolor symmetrical MT were significantly greater in the younger group than in the older group. The older group required a significantly longer completion time for BT and CT than the younger group. Significant correlations were found between one-color BT/CT and dexterity tests, between bicolor BT/CT and dexterity/cognitive tests, and between a bicolor MT and a cognitive test. Questionnaire results revealed that participants regarded BT/CT as easy/interesting tasks, whereas MT was considered a difficult/challenging task. In conclusion, our e-Peg is potentially a useful rehabilitation device that facilitates many tasks related to hand manipulation and attention/executive functions, and a valuable tool for personalized therapy.

Oral care is key to maintaining overall health. Elderly people and people with disabilities who require nursing care can have difficulty in brushing their teeth effectively and may require the assistance of a caregiver. However, to date, an effective method for teaching toothbrushing skills to caregivers and a training system that can be implemented in a non-in-person setting have not been established. Therefore, in this study, we developed a training simulator that enables skill acquisition while learning ideal brushing motion and force information from an arbitrary viewpoint in a virtual reality (VR) space. In this simulator, the position and orientation of the toothbrush as measured by six infrared cameras and the brushing force as measured by a small 6-axis force sensor are displayed in a VR space using computer graphics to match the visual information in the VR space with hand-force information in real space. The experiment was conducted in 10 healthy adults with no specialized skills or knowledge regarding oral care. They were trained to brush the cervical and distal parts of the maxillary central incisors while learning the ideal brushing motion shown by the computer graphics and the appropriate range of brushing force. Afterwards, skills were quantified in terms of brushing motion, brushing force, and plaque removal rate, and the overall effectiveness of the training was evaluated. The results showed that in cervical part brushing, the mean error between the participants' brushing motion and the ideal brushing motion in the brushing direction improved from approximately 4.8 mm before training to 3.9 mm after training. In distal part brushing, the error improved from approximately 3.8 mm to 1.9 mm. Similarly, brushing force improved from approximately 0.8 N before training to the appropriate force of 1.5 N after training for both cervical and distal part brushing. The artificial plaque removal rate improved from 40.3% to 68.7% for cervical part brushing and from 39.5% to 63.3% for distal part brushing, indicating the effectiveness of training using the proposed system. This simulator, which can simultaneously teach both brushing motion and brushing force, is expected to be developed as a new method for teaching toothbrushing skills to caregivers.

For older people who live alone, we previously designed a system that enabled family members at distant locations to monitor television (TV) usage and other activities of the older people, and the system has been used for over 10 years. This system comprises personal computers (PCs) that run on Microsoft Windows^® operating system (OS) and a few sensors. The system forwards the TV usage data of the older person to family members through free e-mail. To operate the PCs safely for a longer period, regular updating of the OS and antivirus environment is necessary. Furthermore, the previous systems connected to the internet should be protected against cyberattacks to ensure information security and continuous system operation. To reduce the needs for system maintenance, we redesigned the existing monitoring system by incorporating sensors and microcomputers with an Arduino integrated development environment. A feasibility test was conducted for the new system, in which a 76-year-old woman and her family participated. Our findings showed that the new system was significantly maintenance-free, and the device size, when installed next to the TV, was approximately 90% smaller than the previous design. In addition, the new system performed as efficiently as the previous system.

Since all of noninvasive optical monitors such as near-infrared spectroscopy are transcutaneous, it is important to consider the optical properties of the skin. The purpose of the present study was to clarify the effects of incident angle and source-detector separation (SDS) on the detection of components in the dermal layer. Here, we developed a novel skin phantom consisting of epidermal and dermal layers with optical properties ranging from 400 to 1600 nm. The phantom was simulated by only water, scatters and absorbers, without agarose, silicone or other materials. The phantom showed reflectance spectra very similar to those of actual human skin by the integrating sphere measurements. Furthermore, an optical system was assembled in which the incident angle could be changed from 20º to 80º and the SDS from 0 to 6 mm independently. For the wavelength range of 400 to 900 nm, the absorption spectrum of hemoglobin in the dermal layer was investigated to assess detectability. For the wavelength range of 900 to 1600 nm, the absorption spectrum was confirmed by including glucose in the dermal layer. The absorbance was calculated from the measured diffuse reflected light intensity. The optimal incident angle and SDS for optical measurements focused on the dermal layer were estimated by the signal-to-noise ratio (SNR). In the wavelength range of 400 to 900 nm, the absorption spectrum of hemoglobin with the highest SNR was obtained at an incident angle of 70° and SDS of 4 mm. In the wavelength range of 900 to 1600 nm, the absorption spectrum of glucose with the highest SNR was obtained at an incident angle of 20° and SDS of 0 mm. These conditions are expected to be optimal for transcutaneous measurement of biomolecules within the dermis using diffuse reflected light of wavelength range from 400 to 1600 nm.

Recently, the number of health-related car accidents in Japan has been increasing. Most health conditions leading to traffic accidents are detectable by analyzing the fluctuation in blood pressure. In addition, cardiopulmonary arrest is an important condition that is linked to car accidents and should be studied along with respiration. Therefore, this study proposes a noncontact measurement system for cardiac dynamics and respiration using a coupled capacitance electrode. The measurement principle of the coupled capacitance electrode utilizes capacitive coupling with the human body. The human body, in particular the heart and the lung, is regarded as a dielectric. The displacement and fluctuation of the dielectric are indicated by capacitance changes in the coupled capacitance electrode. To validate the proposed system, we conducted measurements with the proposed system concurrent with chest circumference measurement using a strain gauge and electrocardiographic recording as references. The results revealed that the proposed system is capable of measuring respiration and cardiac dynamics.

The COVID-19 pandemic has an impact on people's ability to exercise and walk. Decreased exercise habit leads to decreased muscle mass, which may hamper social activities. The decline in exercise habit is a particular challenge among older people, as it may lead to frailty. In this paper, we report that creating arches at the cuboid boneand adductor muscles activates toe movement. We examined whether the arches could be built into shoe insole to correct left–right differences in leg skeletal muscle mass. A prototype insole with arches created at the cuboid boneand transverse head of adductor hallucis musclewas designed, and a prospective cohort study was conducted to examine its effects. Plantar pressure during walking was recorded in 10 subjects, and a 3-month intervention study was conducted in 40 subjects. The results showed an increase in toe pressure from before to after wearing the prototype insoles. In addition, leg skeletal muscle mass increased in the group wearing the prototype insole, and a significant difference compared to the control group was detected by Wilcoxon signed-rank test. The results from this study demonstrate that the prototype insole promotes toe movement and increases leg skeletal muscle mass.

Brain-computer interface (BCI) has attracted attention as a means of assisting patients with intractable neurological diseases to communicate their intentions. However, there are no reports examining the mental tasks that are effective in acquiring physiological signals for BCI, based on evaluation of classification performance and subjective usability of the mental tasks. This study aimed to investigate preferable mental task combinations that could be utilized in the task selection process for near-infrared spectroscopy (NIRS)-based BCIs. We evaluated the classification model performance of brain activation responses using NIRS signals and subjective usability of mental tasks. NIRS signals were measured in 10 healthy adult participants while they performed mental arithmetic task (MA), mental singing task (MS), mental writing task (MW), and mental figure rotation task (MFR), using a block design consisting of a 30-s rest and 30-s task period. For six combinations of mental tasks, binary classification models were constructed using random forest with a dimension reduction method, and the classification performance was evaluated using three-fold cross-validation. We also measured the subjective usability of the mental tasks. The results showed that MW vs. MFR was classified with an average accuracy of 71.5%, and six participants achieved over 70% accuracy. The subjective ratings of state anxiety and acceptability showed that all the mental tasks were rated as acceptable for use in BCI applications. These findings may be utilized as the preliminary communication tasks in the process of selecting appropriate cognitive tasks for BCI for individual users.

In this study, we successfully produced a scaffold-free cell construct for regenerative medicine using the Kenzan method, which involves the fusion of spheroids on a needle array. The cardiac construct fabricated using a three-dimensional bioprinter reproduced heart beating on the needle array, and the resultant displacement of the micro-Kenzan needle was proposed as a non-invasive procedure to analyze the cardiac constructs. Therefore, this study aimed to develop a software that accomplishes this non-invasive analysis by applying image processing technology, using a video of the cardiac construct taken directly overhead the construct. The Lucas–Kanade method was used to calculate the optical flow for tracking the needle tips. The developed software was used to measure the displacement of the needle tip over time, and the results showed that the software was able to evaluate the waveforms and beating direction of the entire or partial cardiac construct.

Motion analysis can be performed during walking by measuring the contact pressure and shear stress acting on the body surface. Although floor reaction force gauges and stress sensors embedded in shoe soles are often used to measure triaxial stress acting on the sole of the foot, the stress acting on the contact interface is difficult to measure directly. In recent years, we have developed thin and flexible triaxial stress sensors that may be placed directly at the contact interface of the body, thus enabling the measurement and evaluation of contact stress in a more natural state. In this study, we developed a compact wearable measurement system that does not restrict the conditions of measurement such as range of motion and motion environment. The compact wearable measurement system consisting of a microcomputer and a sensor measurement circuit was developed for use with triaxial stress film sensors. The signals from the sensor circuit were processed by a program in the microcomputer and recorded on a microSD card to realize a measurement system that does not require cable connection to an external power supply or computer. Sensors were placed on the thenar and heel of the foot, and the usefulness of the system was evaluated by comparing the contact stresses acting on the foot while walking on a flat indoor track. We confirmed the validity of the wearable measurement system by measuring the values of contact pressure and shear stress acting on each measurement part during walking on the flat indoor track. Furthermore, the developed system was applied to gait measurement during walking on snow covered roads. With further improvements in this system, its application in rehabilitation and sports medicine is expected.

The mechanism of hallux valgus (HV) development has not been fully clarified, and a new evaluation method is required. We aimed to establish a method for calculating the rotational moment of the midfoot (RMM) by developing a three-dimensional foot scanner, and to reveal features of HV using this method. A smartphone was used to capture images and analyze regions of the foot in 592 participants. We focused on feature points such as the great toe-first metatarsal head-heel (GFH) angle as the HV angle, the navicular bone, and the centerline of the foot. Navicular-moment-arm (NMA) was defined as the distance between the navicular bone and the centerline of the foot. RMM was calculated from NMA and body weight. The mean values of RMM were 12.3 and 9.4 Nm for male and female participants, respectively. With pronation of the midfoot, the rotational moment and the load on the midfoot increased because of the increase in navicular adduction and axis of bone distance (ABD). ABD and RMM increased with increasing GFH angle quartiles in male and female participants. In particular, ABD and RMM were significantly higher at the fourth quartile of GFH angle. Transverse arch width and height were identified as predictors of GFH angle, both showing high contribution. The navicular bone associated with NMA is controlled mainly by the posterior tibial muscle. Dysfunction of the posterior tibial muscle causes an increase in NMA, leading to an increase in the first and second metatarsal (M1-M2) angle. Therefore, increases in RMM and NMA cause the first metatarsal to pronate and rotate, inducing an increase in the M1-M2 angle. The rotational moment applied to the navicular bone affects the adjacent medial and intermediate cuneiform bones. Particularly, the adduction motion of the navicular bone causes the medial cuneiform bone to rotate in conjunction, possibly inducing relaxation of the tarsometatarsal joint and leading to metatarsus primus varus. Measurement of the foot skeletal structure using a smartphone has the potential of widespread use.

Tear film stability is essential for maintaining the health of the ocular surface. A tear film interferometer allows noninvasive observation of the dynamics of the tear film lipid layer, thereby enabling the evaluation of tear film stability. However, an interferometer is a medical device used only in clinical settings and is therefore not accessible to the general public. Self-checking of tear film stability in daily life may help prevent dry eye. This study investigates diurnal variation in the tear film lipid layer in daily life using a prototype smartphone-based interferometer. The system consists of a luminescent sheet attached to a smartphone camera, which allows the user to easily videorecord the dynamic behavior of the tear film lipid layer. Thirteen participants (5 with and 8 without contact lenses) were asked to record videos of their tear film five times per day. The diurnal variation of interference grade, spread grade, non-invasive break-up time, maximum blink interval, tear meniscus height, and dry-eye-related subjective symptoms evaluated using the visual analog scale were measured. The results revealed a significant decrease in mean tear meniscus height measured at 18:00 compared with 9:00 (P < 0.05) in participants without contact lenses. The mean interference grade at 15:00 and spread grade at 21:00 were significantly greater than those at 9:00 (P < 0.05) in participants with contact lenses. These results suggest that a smartphone-based interferometer is a potentially useful tool for evaluating tear film parameters in daily life.

Human error during surgery can cause unexpected accidents. Elucidating the causes of human error by monitoring the behavior of the surgical team can improve safety. Conventional methods include a questionnaire that is presented to the surgical team, but this is subjective, and its accuracy depends on memory. However, pose estimation is an emergent technology that is able to capture a person's skeletal information. Objective monitoring of group behaviors using pose estimation may assist in identifying the causes of human error. In this study, we utilized the surveillance video images of the entire surgical team to verify the effectiveness of the proposed method in extracting and quantifying group behaviors in order to identify the intraoperative situation. Specifically, we attempted to extract the behaviors focusing on the actions of handing over surgical instruments and the actions that multiple members pay attention to simultaneously. In the proposed method, pose estimation is applied to surveillance camera images to model individual behavior based on the individual's position, facial orientation, body orientation, arm bending, and wrist position. The individual's behavior and ID are then combined and input into an estimation model to extract group behavior. The proposed method was applied to six actual surgical videos to estimate group behaviors. Accordingly, we were able to accurately extract behaviors involving cooperation of multiple members in addition to behaviors involving receiving and delivering, as well as behaviors warranting the attention of multiple members. Further, we successfully identified important points of occurrence of characteristic group behaviors from redundant surgical videos.

Falls in older adults is a major public health issue with approximately one-third of individuals aged 65 or older experiencing at least one fall event annually. Accordingly, there is a need for methods to identify elderly individuals at risk of falls. Methods allowing automated and instantaneous assessment of fall risk would have considerable utility in hospitals and nursing care facilities with staff shortage or time pressure. The present study evaluated models for estimating fall risk from gait characteristics measured during a single gait cycle. As gait images are affected by clothing, skeletal data was recorded using motion capture imaging. Fall risk was determined according to the history of falls within the preceding year. Of 80 healthy subjects aged over 65 years who participated in this study, 45 had experienced falls in the preceding year. Gait features, time series data, and gait energy images were recorded. The area under the receiver operating characteristic curve (AUC) was utilized as a performance measure to evaluate machine learning models. The input of Gait Energy Image data into a 6-layer convolutional neural network (CNN) provided higher accuracy (AUC = 0.67) than other inputs. Visual explanations from the 6-layer CNN created using Eigen-CAM demonstrated that areas associated with step length were predictor for estimating fall risk. Arm swing at heel strike and feet movements were also predictors of fall risk. The models evaluated in this study can be utilized to estimate fall risk from instantaneous measurements, with promising applications in various industries including medical care.

Most sports-assisting technologies have been developed to improve performances only in individual sports such as skiing, batting, and swimming. In team sports, it is important to perceive one's position relative to others. Few studies have focused on team sports, which not only require the motor ability of individual players, but also their perceptual abilities. This pilot study aimed to evaluate the effectiveness of a visual feedback system to improve players' spatial perception relative to others. The visual feedback system was composed of a flying drone that transmitted an image to the participant's smart glasses. The participant was able to see his/her own relative position in real time using the glasses with and without the system. Nine participants tried to position themselves on an imaginary line between two experimenters 30 m away from each other, which simulated the situation of a baseball cutoff man. The results showed that the error in distance between the participants' positions and the line decreased significantly when using the system compared to when not using it. Furthermore, there was also a reduction in positioning error in subsequent trials after participants performed the task using the system, compared to before they used the system. In conclusion, the real-time feedback system from a bird's-eye view has the potential to improve accuracy in spatial perception.

Compound plane-wave imaging (CPWI) is a widely used and investigated imaging technique in medical ultrasound because it provides high quality ultrafast imaging for recent applications such as elastography. CPWI can be either coherent to provide high resolution and reduce sidelobe, or incoherent to provide high speckle homogeneity. To further improve imaging quality, coherence-based factors are used for weighting the output of ultrasound beamformers. This work studied the effects of the number of compounded frames and the step between these frames on the imaging quality produced by coherent and incoherent CPWI in the presence of the generalized coherence factor (GCF). The quality of the produced images of two different RF datasets was assessed in two different scenarios, in addition to conducting cyst phantom simulations. Results showed that the amount of image contrast improved by GCF increased, while the amount of resolution improved by GCF decreased, with the increase in step between frames. The same results were obtained in both types of CPWI. On the other hand, increasing the number of frames had almost no effect on the amounts of improvement provided by GCF. When CPWI is used in ultrafast imaging, it is important to monitor frame rates as well as imaging quality; these two factors are, respectively, inversely and directly proportional to the number of compounding frames. Therefore, the results of this research provide guidelines for accurate angle selection for CPWI so that a trade-off between imaging quality and frame rate is achieved.

As the impact of population aging becomes increasingly serious, more problems emerge with regard to medical and long-term care for the older individuals, which require solution. In long-term care, accidental falls frequently occur and are a significant cause of serious injuries and bed confinement due to leg fractures. Older people are especially prone to fall out of bed when they try to get up on their own. To reduce the occurrence of such accidents, one important solution is to detect the getting-up behavior of older individuals and to alert nursing staff to come and check. Although many studies have proposed various solutions such as the use of wearable devices and vision-based sensors, there are many issues in practical application. The complexity of device installation, high initial cost, and maintenance problems have restricted most care facilities to using fall prevention systems with mediocre results, such as pressure pad sensors. In this paper, we propose a fall prevention system based on a single camera (with infrared function) and infrared reflective sheets (IR sheets). The system detects the IR sheets placed on the shoulders of an individual and obtains position data. The relative positions of the IR sheets can be used to identify the state of the person on the bed. To improve the identification ability of the system, we propose to define an identification area. To identify the state more accurately, we propose to establish sub-areas within the identification area. We conducted experiments by recruited 19 subjects. The data of 6 subjects were used to construct the sub-areas. The other 13 subjects participated in testing the ability of the system in identifying the various states of the person in bed. Compared with the performance of other studies, our experimental results demonstrate that our system has a high identification rate, in addition to being low-cost and easy to set up.

Transcranial magnetic stimulation (TMS) is a widely used non-invasive neurostimulation technique in neuroscience and in the treatment of psychiatric disorders. By placing a TMS coil over a patient's head, neurons in the brain can be electromagnetically stimulated through the induction of an electric field (E-field). Accurate estimation of the E-field induced in a patient's head is crucial for determining the stimulated area of the brain. The electromagnetic simulation for E-field estimation involves two processes: the development of a volume conductor model (VCM) to determine the electrical conductivity at each position of the brain from a head magnetic resonance (MR) image, and the computation of the E-field on the VCM. Currently, neither of these processes can be performed in real-time. Achieving real-time estimation would greatly assist in determining the appropriate coil position and direction to stimulate the target regions in the patient's brain. In recent years, several methods utilizing deep neural networks (DNNs) have been proposed to estimate E-fields from MR images in real-time. These methods construct a regressor of the E-field using a set of simulated E-fields as training data to estimate the E-field. However, the reliability of these regressors in clinical applications could be improved by incorporating uncertainty estimation of the regressed variables, although this has not been reported. In this study, we enhanced the accuracy of E-field strength estimation by first regressing the E-field and then computing the norm of the E-field vectors, instead of directly regressing the E-field strength. In addition, we investigated the statistical uncertainty of the regressed E-fields using DNN. It should be noted that the E-fields estimated by the regressors are random variables. To evaluate the uncertainty of this application, we employed MCDropout, a well-known Bayesian estimation method. The uncertainty of the regressed E-field was evaluated for each anatomical tissue of the brain, to examine the relationship between uncertainty and depth from the coil. The experimental results of this evaluation are presented quantitatively.

Alpha-rhythm enhancement during memory maintenance has been explained as active inhibition of task-irrelevant visual inputs because the enhancement is generally observed in occipital visual areas. Moreover, in a sequential memory task, items encoded at an early stage must be maintained in parallel to the encoding of subsequent memory items. To reveal the role of the alpha-rhythm in multiple memory processes, magnetoencephalograms of 14 young participants (age, mean ± SD = 21.1 ± 1.1 years) were recorded during a sequential memory task. Participants memorized seven sequentially presented arrow directions. Arrow directions were randomized in the memory trials, whereas they were fixed in the control trials. The time course was divided into four periods: beginning (0–1.2 s: 1st and 2nd arrows presented), midterm (1.2–3.0 s: 3rd–5th arrows), ending (3.0–4.2 s: 6th and 7th arrows), and maintenance (4.2–5.2 s: before recall cue presentation). The source amplitudes of the alpha-rhythm were analyzed by three-way repeated-measures ANOVA (Memory/Control × Brain region [n = 68] × Time period [n = 4]) with post hoc analysis. In many brain regions, alpha-rhythm amplitude was significantly enhanced in the Memory condition than in the Control condition. These enhancements were distributed widely across brain regions in the beginning period but gradually converged to occipital areas toward the maintenance period. Our results suggest that even if alpha-rhythm enhancement is caused by active inhibition, it is not always solely attributable to the inhibition of visual inputs. Additionally, we compared the time courses of alpha-rhythm amplitude obtained in young participants in this study with those recorded in older participants in a previous study. In the previous work, it was suggested that precuneus activity was essential for memory performance in older people. However, for the young participants in this study, precuneus activity was highly inhibited and not related to memory performance.

Implantable ventricular assist devices (iVADs) are commonly used to treat patients with severe heart failure. To ensure safe and reliable operation of iVADs, it is crucial to develop a method for estimating the flow rate (Q) of the centrifugal blood pump used in iVAD without relying on a flowmeter. To address this need, we previously developed a flow rate estimation method (FEM-r) that focuses on the correlation between Q and the levitation position (r) of an impeller suspended by a magnetic bearing in a centrifugal blood pump. The FEM-r maintains a high level of estimation accuracy even when changes in blood viscosity are expected during treatment. This advantageous characteristic distinguishes FEM-r from the conventional method (FEM-I_MT) that employs motor current as an estimation index. However, a drawback of FEM-r is the requirement for an additional displacement sensor to measure r. In this study, we propose a solution to this issue by leveraging the correlation between r and the balance of currents (I_EMs) flowing through the electromagnets in a magnetic bearing. Thus, we propose an estimation method called FEM-I_EM, which utilizes the balance of I_EMs as a flow rate estimation index. The balance of I_EMs can be obtained using the minimum number of sensors required for iVAD control. First, we developed an estimation equation for FEM-I_EM and determined its estimation coefficients by multiple regression analysis based on the data obtained from experiments using a mock loop. The root mean square error (RMSE) and determination coefficient (R²) of the multiple regression analysis were 0.293 L/min and 0.965, respectively. The RMSE between the estimated and measured Q values were 0.284, 0.347, and 1.069 L/min for FEM-I_EM, FEM-r, and FEM-I_MT, respectively, indicating that the accuracy of FEM-I_EM was comparable to that of FEM-r and significantly higher than that of FEM-I_MT. Thus, the simple FEM-I_EM proposed in this study demonstrates excellent performance for clinical use, allowing accurate flow rate estimation of a centrifugal blood pump without requiring additional position sensor, viscosity measurement, or compensation method, even when the blood viscosity fluctuates.

The prevention of dementia is a pressing issue on a global scale. One of the most important recommendations in the World Health Organization's guidelines entitled “Risk reduction of cognitive decline and dementia” (PA guidelines) is to take part in physical activity, “maintaining at least 3 metabolic equivalents of task (METs) per exercise for at least 10 minutes”. The authors believe that adding a musical performance to aerobic exercise makes the exercise more appealing, and provides the motivation to exercise and the encouragement to continue. The purpose of this study was to develop a new electronic musical instrument, Cymis-Foot, which has a built-in music score and has two functions. The first is that novices can play their favorite pieces easily using their feet to repeatedly push down onto a balloon, i.e. foot-stomping, while in a sitting position. When they do this, they force the pressure in the balloon to reach a threshold, and at a constant tempo the music is played smoothly. The second is that the PA guidelines can be easily satisfied with a performance of the Cymis-Foot. The METs can be calculated, as described in a previous report, by monitoring the heart rate during the performance and at rest. Eleven cognitively normal novices in their 20 s were able to perform pieces easily at between 40 and 160 bpm. The guidelines were still satisfied with foot-stomping from a greater height at a slower tempo, whereas five participants reported difficulty in satisfying the PA guidelines at 40 bpm.