The Use of Wearable Technology in Providing Assistive Solutions for Mental Well-Being

The main goal of this manuscript is to provide an extensive literature review and analysis of certain biomarkers, which are frequently used to identify stress, anxiety, and other emotions, leading to potential solutions for the monitoring of mental wellness using wearable technologies. It is possible to see the impacts of several biomarkers in detecting stress levels and their effectiveness with an investigation into the literature on this subject. Biofeedback training has demonstrated some psychological effects, such as a reduction in anxiety and self-control enhancement. This survey demonstrates backed up by evidence that wearable devices are assistive in providing health and mental wellness solutions. Because physical activity tracing would reduce the stress stressors, which affect the subject's body, therefore, it would also affect the mental activity and would lead to a reduction in cognitive mental load.

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review

Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.

ECG classification system based on multi-domain features approach coupled with least square support vector machine (LS-SVM)

Developing a robust authentication and identification method becomes an urgent demand to protect the integrity of devices data. Although the use of passwords provides an acceptable control and authentication, it has shown much weakness in terms of speed and integrity, which make biometrics the ideal authentication solution. As a result, electrocardiogram (ECG) signals have received a great attention in most authentication systems due to the individualized nature of the ECG signals that make them difficult to counterfeit and ubiquitous. In this paper, we propose a new model for ECG verification using multi-domain features coupled with a least square support vector machine (LS-SVM). Two types of features are investigated to find the best set of features to individual from ECG signals. Time domain and frequency domain features based on optimized Triple Band filter bank are extracted from ECG signals. The extracted features are investigated to figure out the best relevant features and remove the redundant ones. The selected features are fed to three classifiers, including Least Square Support Vector Machine (LS-SVM), K-means, and K-nearest. The obtained results have shown that our ECG biometric authentication system outperforms existing methods. The proposed model obtained an average of accuracy of 88%, 95% with time and frequency features, respectively, while it recorded 99% when a combination of time and frequency features are used to classify ECG signals. A public dataset is used to assess the proposed model.

A scoping review on monitoring mental health using smart wearable devices

With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Long-term and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health.

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.

An Intelligent Clustering-Based Routing Protocol (CRP-GR) for 5G-Based Smart Healthcare Using Game Theory and Reinforcement Learning

With advantages such as short and long transmission ranges, D2D communication, low latency, and high node density, the 5G communication standard is a strong contender for smart healthcare. Smart healthcare networks based on 5G are expected to have heterogeneous energy and mobility, requiring them to adapt to the connected environment. As a result, in 5G-based smart healthcare, building a routing protocol that optimizes energy consumption, reduces transmission delay, and extends network lifetime remains a challenge. This paper presents a clustering-based routing protocol to improve the Quality of services (QoS) and energy optimization in 5G-based smart healthcare. QoS and energy optimization are achieved by selecting an energy-efficient clustering head (CH) with the help of game theory (GT) and best multipath route selection with reinforcement learning (RL). The cluster head selection is modeled as a clustering game with a mixed strategy considering various attributes to find equilibrium conditions. The parameters such as distance between nodes, the distance between nodes and base station, the remaining energy and speed of mobility of the nodes were used for cluster head (CH) selection probability. An energy-efficient multipath routing based on reinforcement learning (RL) having (Q-learning) is proposed. The simulation result shows that our proposed clustering-based routing approach improves the QoS and energy optimization compared to existing approaches. The average performances of the proposed schemes CRP-GR and CRP-G are 78% and 71%, respectively, while the existing schemes, such as FBCFP, TEEN and LEACH have average performances of 63%, 48% and 35% accordingly.

SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.

Effect of a behavioral intervention on anxiety and perceived performance of non-technical skills during surgical simulations

BACKGROUND

Surgical trainees experience intrinsic stress and anxiety during high-acuity clinical situations which can negatively impact performance. Emerging data suggests that education in mindfulness-based coping techniques may improve performance. We evaluated the effects of a stress recovery intervention on novice trainees' perceived level of anxiety during an intentionally stressful simulation.

METHODS

Participants were recruited from surgical intern classes over three consecutive years. All participants completed a simulation intentionally designed to evoke a stress response. Participants then completed a stress recovery intervention or received no additional training. All participants then completed a second novel simulation.

RESULTS

Intervention participants had significantly higher self-reported ability to manage stress (intervention 2.4 to 3.6, p < 0.01; control 2.8 to 3.3, p = 0.06), and stop, think, and observe (intervention 2.5 to 3.7, p < 0.01; control 2.6 to 3.3, p = 0.08) during the second simulation. Both groups also had significantly lower levels of state anxiety during the second simulation as compared to the first (intervention 45.1 vs 59.3, p < 0.01; control 49.3 vs 57.4, p < 0.05). During the second simulation, trainees in both groups reported improvements in perceived abilities to: recognize stress (intervention 2.7 to 4.1, p < 0.01; control 2.9 to 3.6, p < 0.05), communicate with and lead their team (intervention 2.4 to 3.3, p < 0.05; control 2.3 to 3.3, p < 0.01), and to prioritize, plan, and prepare (intervention 2.1 to 3.1, p < 0.05; control 2.1 to 3.0, p < 0.01).

CONCLUSION

Our research shows that a brief intervention was associated with a significant increase in trainee ability to both recognize internal stress and engage in proactive coping mechanisms. This research also shows that while repeated stress-inducing simulations may themselves decrease perceived anxiety levels in novice surgical trainees, training in coping strategies may potentiate this effect.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Analysis of user behavior on the website of a university eye hospital in Germany

Medical information websites are usually targeted toward patients, physicians, and medical students. Most of the latest studies researched the usability of such websites. However, reports on user numbers and behavior are rare.The goal of our study is to analyze the utility of a website of a large tertiary referral center in ophthalmology in terms of various target audiences (patients, applicants, medical students, referring ophthalmologists).The web access data from our institutional website was assessed with Google Analytics. Data collection started in 2016.From 2016 to 2018, we counted a total of 763,942 page views. The overall number of users dropped from 81,572 to 67,740. This drop's onset correlates with a change in the website structure. All target audience pages received constant traffic from 2016 to 2019, with the patients' and doctors' sites attracting the most traffic. The pages for medical students and job applicants, although not viewed often, revealed a long session duration.Our website is used by all our target audiences. The behavior and the user numbers of each target group differ. Changes to a website's structure can influence the number of users and their behavior. It is not possible to make a direct comparison to other institutions' websites as there are so few similar reports available. By adding more parameters to the analytics profile in a prospective setting, more detailed analyses of user behavior may be possible in the future.

StressFoot: Uncovering the Potential of the Foot for Acute Stress Sensing in Sitting Posture

Stress is a naturally occurring psychological response and identifiable by several body signs. We propose a novel way to discriminate acute stress and relaxation, using movement and posture characteristics of the foot. Based on data collected from 23 participants performing tasks that induced stress and relaxation, we developed several machine learning models to construct the validity of our method. We tested our models in another study with 11 additional participants. The results demonstrated replicability with an overall accuracy of 87%. To also demonstrate external validity, we conducted a field study with 10 participants, performing their usual everyday office tasks over a working day. The results showed substantial robustness. We describe ten significant features in detail to enable an easy replication of our models.

Effect of cognitive-behavioral therapy with music therapy in reducing physics test anxiety among students as measured by generalized test anxiety scale

BACKGROUND

The study determined the effect of cognitive-behavioral therapy (CBT) with music in reducing physics test anxiety among secondary school students as measured by generalized test anxiety scale.

METHODS

Pre-test post-test randomized control trial experimental design was adopted in this study. A total of 83 senior secondary students including male (n = 46) and female (n = 37) from sampled secondary schools in Enugu State, Nigeria, who met the inclusion criteria constituted participants for the study. A demographic questionnaire and a 48-item generalized test anxiety scale were used for data collection for the study. Subjects were randomized into treatment and control groups. The treatment group was exposed to a 12-week CBT-music program. Thereafter, the participants in the treatment group were evaluated at 3 time points. Data collected were analyzed using repeated measures analysis of variance.

RESULTS

The participants who were exposed to CBT-music intervention program significantly had lower test anxiety scores at the post-treatment than the participants in the control group. Furthermore, the test anxiety scores of the participants in the CBT-music group were significantly lower than those in the control group at the follow-up measure. Thus, the results showed a significant effect of CBT with music in reducing physics test anxiety among secondary school students.

CONCLUSION

We concluded that CBT-music program has a significant benefit in improving the management of physics test anxiety among secondary school students.

Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction

Electrocardiogram (ECG) is a record of the heart's electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq -7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system.

Prolonged international normalized ratio and vascular injury at divisional level predict embolization failures of patients with iatrogenic renal vascular injuries

Transcatheter arterial embolization (TAE), as an alternative to surgery for iatrogenic renal vascular injury (IRVI), may have unsatisfactory outcomes. Nonetheless, there is inadequate information regarding the predictors of TAE outcomes for IRVI in the literature. The aim of this retrospective study was to investigate the predictors of TAE outcomes for IRVI. Of 47 patients, none had major complications, 17 (36.2%) patients had minor complications, and none suffered significant renal function deterioration after TAE. Technical success and clinical success were 91.5% and 93.6%, respectively. Technical failure was associated with older age, thrombocytopenia, prolonged international normalized ratio (INR) and divisional IRVI. Clinical failure was associated with kidney failure, use of steroids, prolonged INR, and divisional IRVI. In addition, prolonged INR was a significant predictor of technical failure. This implies that aggressive measures to control the INR prior to TAE are warranted to facilitate technical success, and technical success could then be validated on post-TAE images. Furthermore, divisional IRVI was a predictor of clinical failure. Thus, divisional IRVI should undergo surgery first since TAE is prone to clinical failure. The avoidance of clinical failure is validated if divisional IRVI does not need further intervention.

IoT-Enabled Dual-Arm Motion Capture and Mapping for Telerobotics in Home Care

With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the fastest growing fields of robotics. The combination of robot capabilities with human intelligence, for example, telerobotics for home care, is gradually showing promising potentials. In this paper, the Home-TeleBot system, a generalized IoT-enabled telerobotic architecture designed to support home-centric healthcare system, is proposed. In particular, the implementation of it is realized by integrating human-motion-capture subsystem with robot-control subsystem. The dual-arm cooperative robot, YuMi, imitates human motion captured by a set of wearable inertial motion capture devices to complete tasks. The proposed approach using workspace mapping and path planning of robot manipulators, facilitates telerobot to execute tasks in a natural and human-like way. Based on the constant of proportionality calculated by comparing the human original workspace with the robot original workspace, the workspace mapping is achieved by making assumptions of the distance between end-effectors (human hands, robot's grippers) and shoulders. Additionally, robot manipulators' path is planned by setting virtual obstacles to constrain robot motion, which aims to improve the performance of robot's human-like motion. As a specific example of application, we apply the proposed architecture to a fetching task based on dual-arm motion capture and mapping for telerobotics in home care.

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities

BACKGROUND

Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is an addition to biometric authentication systems, which has many advantages over others. In this paper, we study the performance of a single channel brainwave-based authentication systems and select optimum channels based on mental activities.

METHODS

In this study, we used a dataset with five mental activities with seven subjects (325 samples). The EEG based authentication system includes three pre-processing steps, feature extraction, and classification. Features for Subject Authentication, are obtained from discrete Fourier transform, discrete wavelet transform, autoregressive modeling, and entropy features. Then these features are classified using the Neural Network, Bayesian network and Support Vector Machine.

RESULTS

We achieved accuracy in the range of 97-98% mean accuracy with Neural Network classifier for single-channel authentication system with optimum electrode placement for mental activity. We also analyzed the authentication system independently from the type of mental activity and chose channel O₂ as the optimum channel with an accuracy of 95%.

CONCLUSIONS

Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities.

IoT with cloud based lung cancer diagnosis model using optimal support vector machine

In the last decade, exponential growth of Internet of Things (IoT) and cloud computing takes the healthcare services to the next level. At the same time, lung cancer is identified as a dangerous disease which increases the global mortality rate annually. Presently, support vector machine (SVM) is the effective image classification tool especially in medical imaging. Feature selection and parameter optimization are the effective ways to improve the results of SVM and are conventionally resolved individually. This paper presents an optimal SVM for lung image classification where the parameters of SVM are optimized and feature selection takes place by modified grey wolf optimization algorithm combined with genetic algorithm (GWO-GA). The experimentation part takes place on three dimensions: test for parameter optimization, feature selection, and optimal SVM. For assessing the performance of the presented approach, a benchmark image database is employed which comprises of 50 low-dosage and stored lung CT images. The presented method exhibits its superior results on all the applied test images under several aspects. In addition, it achieves average classification accuracy of 93.54 which is significantly higher than the compared methods.

A group-focused rational emotive behavior coaching for management of academic burnout among undergraduate students: Implications for school administrators

BACKGROUND

High-stress level affects students' health and many of them experiencing high levels of stress are at risk of burnout. School administrators are often concerned about the experiences and negative effects of burnout among students and staff. Burnout is described as a psychological reaction to chronic stress. The aim of the current study was to investigate the effect of a group-focused intervention (rational emotive behavior coaching, REBC) on academic burnout among undergraduate students attending public universities in Southeast Nigeria.

METHODS

A group randomized controlled trial design was adopted for this study. A total of 52 convenient samples of undergraduate students (with a high degree of burnout symptoms) took part in the research. We used a group REBT program manual for the management of burnout which was complemented with REBC techniques. Data were gathered with the aid of the perceived stress scale (PSS-10) and Oldenburg Burnout inventory-student (OLBI-S). Data were analyzed using ANOVA and paired t test at .05 probability level.

RESULTS

Results showed that the group-focused REBC program significantly alleviated burnout symptoms among students in the treatment group compared to students in the control group as measured by OLBI-S subscales: exhaustion (F(1,51) = 41.789, P = .000, (Equation is included in full-text article.), ΔR = 0.634, SE = 1.00), and disengagement (F(1,51) = 196.036, P = .000, (Equation is included in full-text article.), ΔR = 0.869, SE = 0.69). The students who benefitted from the group-focused REBC program maintained reduced symptoms of burnout after three months when the researchers conducted a follow-up as measured by OLBI-S subscales: exhaustion (F(1,51) = 34.012, P = .000, (Equation is included in full-text article.), ΔR = 0.467, SE = 1.21), and disengagement (F(1,51) = 108.941, P = .000, (Equation is included in full-text article.), ΔR = 0.765, SE = 0.85).

CONCLUSION

This research indicates that group-focused REBC can be applied to reduce burnout symptoms among undergraduate students. The group-focused REBC intervention may be adapted to overcome employee burnout and school administrators' burnout. Researchers may need to investigate the possibility of storing and harnessing data from studies on REBC and burnout and delivering computer-based/internet REBC program following evidence-based computing strategies and principles.

Therapeutic adherence and coping strategies in patients with multiple sclerosis: An observational study

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder affecting the central nervous system and targets the myelin sheaths around nerves. Local problem: Medical advances have enabled patients to lead a better quality of life (QoL) than before. However, because of its chronicity and unpredictability, it remains a very challenging disease for both patients and their families, as it involves the continued use of medication to slow down progression. The aim of this study is to assess drug adherence in patients with MS. In particular, we will examine how the way drugs are administered (oral or injective) affects compliance with therapy, including the correlation with coping strategies and the QoL of each patient.We enrolled 88 patients with MS, divided into 2 groups according to therapy (injective or oral). The Morisky Medication Adherence scale was administered to evaluate adherence to treatment, the MS QoL 54 to estimate mental and physical health, and Brief coping orientation to problems experienced Inventory for coping strategies.The results showed that in both groups the patients showed a good therapeutic alliance and trust in treatment. In particular, a correlation has been found between therapeutic adherence, adaptive coping strategies, and mental health when drug therapy is administered by injection. In conclusion, this result suggests that for patients receiving injection treatment to have greater adherence to therapy, appropriate coping strategies and good mental health must be developed in order for patients receiving injection therapy to have greater adherence to therapy; they need to develop appropriate coping strategies and good mental health to address this mode of administration successfully.

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

A novel vision-based system for quantitative analysis of abdominal aortic aneurysm deformation

BACKGROUND

In clinical diagnostics, combination of different imaging techniques is applied to assess spatial configuration of the abdominal aortic aneurysm (AAA) and deformation of its wall. As deformation of aneurysm wall is crucial parameter in assessing wall rupture, we aimed to develop and validate a Non-Invasive Vision-Based System (NIVBS) for the analysis of 3D elastic artificial abdominal aortic models. 3D-printed elastic AAA models from four patients were applied for the reconstruction of real hemodynamic. During experiments, the inlet boundary conditions included the injection volume and frequency of pulsation averaged from electrocardiography traces. NIVBS system was equipped with nine cameras placed at a constant distance to record wall movement from 360^o angle and a dedicated set of artificial lights providing coherent illumination. Additionally, self-prepared algorithms for image acquisition, processing, segmentation, and contour detection were used to analyze wall deformation. Finally, the shape deformation factor was applied to evaluate aorta's deformation. Experimental results were confronted with medical data from AngioCT and 2D speckle-tracking echocardiography (2DSTE).

RESULTS

Image square analyses indicated that the optimal distance between the camera's lens and the investigated object was in the range of 0.30-0.35 m. There was approximately 1.44% difference observed in aneurysm diameters between NIVBS (86.57 ± 5.86 mm) and AngioCT (87.82 ± 6.04 mm) (p = 0.7764). The accuracy of developed algorithm for the reconstruction of the AAA deformation was equal to 98.56%. Bland-Altman analysis showed that the difference between clinical data (2DSTE) and predicted wall deformation (NIVBS) for all patients was 0.00 mm (confidence interval equal to 0.12 mm) for aneurysm size, 0.01 mm (confidence interval equal to 0.13 mm) and 0.00 mm (confidence interval equal to 0.09 mm) for the anterior and posterior side, as well as 0.01 mm (confidence interval equal to 0.18 mm) and 0.01 mm (confidence interval equal to 0.11 mm) for the left and right side. The optimal range of camera's lens did not affect acquired values.

CONCLUSIONS

The NIVBS with proposed algorithm that reconstructs the pressure from surrounding organs is appropriate to analyze the AAAs in water environment. Moreover, NIVBS allowed detailed quantitative analysis of aneurysm sac wall deformation.

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Developing a Health Risk Evaluation Method for Triple H

The development of a health evaluation system from human-related data is an important issue in preventive medicine. Previously, most studies have focused on disease assessment and prevention in patients. However, even if certain risk factors are all within normal ranges, individuals may not necessarily be completely healthy. This study focused on healthy individuals to develop a new index to assess health risks; this index can be used for the prevention of multiple diseases in healthy people. The kernel density technique was proposed to estimate the distribution of common risk factors and to develop a health risk index. A dataset of hypertension, hyperlipidemia, and hyperglycemia (Triple H) data from the National Health Insurance Research Database in Taiwan was used to demonstrate the proposed analytical process. The results of risk factor changes after six weeks of exercise were used to calculate the health risk index. The results showed that the subjects experienced a 7.29% reduction in their health risk index after the exercise intervention. This finding demonstrates the potential impact of an important reference index on quantifying the effect of maintenance in healthy people.

Effects on the pulmonary hemodynamics and gas exchange with a speed modulated right ventricular assist rotary blood pump: a numerical study

Rotary blood pumps (RBPs) are the newest generation of ventricular assist devices. Although their continuous flow characteristics have been accepted widely, more and more research has focused on the pulsatile modulation of RBPs in an attempt to provide better perfusion. In this study, we investigated the effects of an axial RBP serving as the right ventricular assist device on pulmonary hemodynamics and gas exchange using a numerical method with a complete cardiovascular model along with airway mechanics and a gas exchange model. The RBP runs in both constant speed and synchronized pulsatile modes using speed modulation. Hemodynamics and airway O₂ and CO₂ partial pressures were obtained under normal physiological conditions, and right ventricle failure conditions with or without RBP. Our results showed that the pulsatile mode of the RBP could support right ventricular assist to restore most hemodynamics. Using speed modulation, both pulmonary arterial pressure and flow pulsatility were increased, while there was only very little effect on alveolar O₂ and CO₂ partial pressures. This study could provide basic insight into the influence of pulmonary hemodynamics and gas exchange with speed modulated right ventricular assist RBPs, which is concerned when designing their pulsatile control methods.

The feasibility of 1-stop examination of coronary CT angiography and abdominal enhanced CT

This study aims to evaluate the feasibility of performing coronary computed tomography angiography (CCTA) and abdominal enhanced computed tomography (CT) with 1-time injection of the agent.CCTA images (right coronary artery, left anterior descending coronary artery, and left circumflex coronary artery) were collected from 20 patients who completed a 1-stop combined examination of CCTA and abdominal enhanced CT (group A), 20 patients who only underwent abdominal enhanced CT (group B1), and 20 patients who only underwent CCTA (group B2). These images were interpreted using the 5-point Likert scale system by 2 experienced radiologists, and abdominal images were observed for breathing artifact. CT value, signal-to-noise ratio (SNR), and CTDI were recorded and compare among the 3 groups.The difference in image quality of the coronary and total volume of the contrast agent between group A and group B1 was not statistical significant (P > .05). The CT value and SNR in group B1 (CCTA) (CT: 394.65 ± 59.23, SNR: 17.38 ± 4.13) increased, compare with Group A (CT: 360.35 ± 34.16, SNR: 13.76 ± 1.84, P = .03, .01), while CTDI was undifferentiated between group A (17.14 ± 6.20) and group B1 (18.38 ± 9.79) (P = .64). The difference in CT value and SNR at the arterial phase and CT value at the venous phase between group A (abdomen) and group B2 were statistically significant, the CTDI in group A (9.09 ± 1.05) increased, compared with group B2 (8.23 ± 1.33) (P = .03), and SNR at the venous phase in group B2 (12.50 ± 2.43) increased, compared with group A (10.89 ± 2.03) (P = .03).Revolution CT can capture full images and very rapidly switch to the scan mode, enabling a 1-stop axial CCTA and enhanced helical abdominal scan. The 1-stop combined scan resulted in a satisfactory image quality, which reduced the contrast agent dose and simplified the workflow.The 1-stop combined scan allows for the high success rate of the examination, reduces the number of examinations, and decreases the dose and risk of injection of the contrast agent. This would be helpful for patients to obtain diagnostic images in time.

L1 incidence reflects pelvic incidence and lumbar lordosis mismatch in sagittal balance evaluation

Retrospective study.To investigate the radiologic and geometrical association between L1 incidence (L1I) with pelvic incidence/lumbar lordosis (PI/LL) mismatch and T1 incidence (T1I) with PI/LL/thoracic kyphosis (TK) mismatch.The relationship between PI and LL is not clear, and it might be because of the absence of a direct radiologic parameter to represent PI/LL mismatch. To the best of our knowledge, this is the first report on a direct radiologic parameter for representing PI/LL mismatch.This study is a retrospective review of 146 patients who underwent anteroposterior and lateral standing radiographs of the whole spine. L1I was defined as the angle between the line perpendicular to the L1 upper endplate and the line connecting the midpoint of the sacral endplate to the center of both femoral heads. T1I was defined as the angle between the line perpendicular to the T1 upper endplate and the line connecting the midpoint of the sacral endplate to the center of both femoral heads. Both were validated using the Pearson correlation coefficient and linear regression analysis.Radiologically measured L1I and T1I were coterminous with calculated measurements of ΔPI/LL and ΔPI/LL/TK in terms of means and standard deviations, respectively. Excellent correlations were found between L1I and ΔPI/LL, and T1I and ΔPI/LL/TK (R = 0.997, P < .01; R = 0.981, P < .01, respectively). In linear regression analysis, the slope and intercept of L1I were 0.991 and -0.041, with a predictability of 99.4% (R = 0.994), and those of T1I were 0.990 and -0.026, with a predictability of 99.0% (R = 0.990), respectively.L1I and T1I were strongly correlated with PI/LL mismatch and PI/LL/TK mismatch, respectively. L1I and T1I are direct parameters that represent PI/LL mismatch and PI/LL/TK mismatch. They would be useful in analyzing sagittal balance.

LEVEL OF EVIDENCE

Level 3.