Roseespensen4336

The maximum absolute error on the measuring of distances was 0.64 cm. On angles related to the horizontal was 0.70 degrees and for angles concerning the vertical was 0.76 degrees.Clinical Relevance-By utilizing LAM system all three views were evaluated in less than a minute without counting the time for putting on the markers. The results obtained suggest that the system presents trustworthy results, which reduce considerably the time of carrying out posture evaluations where results are measurable, repeatable and away from the evaluator's subjectivity.Stress is a common issue in today's society and can be caused by a variety of triggers in activities such as work or driving. Various negative consequences can arise of stress such as reduced job productivity, sleep disorders, or physiological diseases like depression or anxiety. A popular approach to manage stress is voluntary deep and slow breathing. However, deliberate deep breathing requires conscious attention and effort, and thus often interferes with daily activities such as working and driving. We present a system that monitors the user's breathing in real-time and provides rhythmical feedback to support effortless and unconscious slow breathing in everyday-life. Our system comprises three feedback modes 1.) acoustic feedback, 2.) haptic feedback, and 3.) mixed feedback combining both modalities. We apply our system in a driver setting and conduct a user study with twelve participants to evaluate the effects of our intervention on users' physiology and perception. We find that acoustic and mixed guiding can reduce breathing pace without affecting focus, which suggests that subtle rhythmical feedback is a promising approach to reduce breathing pace and thus counteract stress.Next to higher data rates and lower latency, the upcoming fifth-generation mobile network standard will introduce a new service ecosystem. Concepts such as multi-access edge computing or network slicing will enable tailoring service level requirements to specific use-cases. In medical imaging, researchers and clinicians are currently working towards higher portability of scanners. This includes i) small scanners to be wheeled inside the hospital to the bedside and ii) conventional scanners provided via trucks to remote areas. Both use-cases introduce the need for mobile networks adhering to high safety standards and providing high data rates. These requirements could be met by fifth-generation mobile networks. In this work, we analyze the feasibility of transferring medical imaging data using the current state of development of fifth-generation mobile networks (3GPP Release 15). We demonstrate the potential of reaching 100Mbit/s upload rates using already available consumer-grade hardware. Furthermore, we show an effective average data throughput of 50Mbit/s when transferring medical images using out-of-the-box open-source software based on the Digital Imaging and Communications in Medicine (DICOM) standard. learn more During transmissions, we sample the radio frequency bands to analyse the characteristics of the mobile radio network. Additionally, we discuss the potential of new features such as network slicing that will be introduced in forthcoming releases.Mobile technologies, including applications (apps) and wearable devices, are playing an increasingly important role in health monitoring. In particular, apps are becoming a critical component of m-health, which promises to transform personalized care management, optimize clinical outcomes, and improve patient-provider communication. They may also play a central role in research, to facilitate rapid and inexpensive collection of repeated data, such as momentary clinical, physiological, and/or behavioral assessments and optimize their sampling. This is particularly important for measuring systems/processes with characteristic temporal patterns, e.g., circadian rhythms, which need to be adequately sampled in order to be accurately estimated from discrete measurements. Temporal sampling of these patterns may also be critical for elucidating their modulation by pathological events. This paper presents a novel app, developed with the overarching goal to optimize repeated salivary hormone collection in pediatric patients with epilepsy through improved patient-investigator communication and enhanced alerts. The ultimate goal of the app is to maximize regularity of the data collection (up to 8 samples/day for ~4-5 days of hospitalization) while minimizing intrusion on patients during clinical monitoring. In addition, the app facilitates flexible collection of data on stress and seizure symptoms at the time of saliva sampling, which can then be correlated with hormone levels and physiological changes indicating impending seizures.Respondent-driven sampling (RDS) is a popular method for surveying hidden populations based on friendships and existing social network connections. In such a survey the underlying hidden network remains largely unknown. However, it is useful to estimate its size as well as the relative proportions of surveyed features. The fact that linked network participants are likely to share common features is called homophily, and is an important property in understanding the topology of social networks. In this paper we present a methodology that scales up RDS data to model the underlying hidden population in a way that preserves multiple homophilies among different features. We test our model using 46 features of the population sampled by the SATHCAP RDS survey. Our network generation methodology successfully preserves the homophilic associations in a randomly generated Barabasi-Albert network. Having created a realistic model of the expanded SATHCAP network, we test our model by simulating RDS surveys over it, and comparing the resulting sub-networks with SATHCAP. In our generated network, we preserve 85% of homophilies to under 2% error. In our simulated RDS surveys we preserve 85% of homophilies to under 15% error.This paper presents a method for estimating the overall size of a hidden population using results from a respondent driven sampling (RDS) survey. We use data from the Latino MSM Community Involvement survey (LMSM-CI), an RDS dataset that contains information collected regarding the Latino MSM communities in Chicago and San Francisco. A novel model is developed in which data collected in the LMSM-CI survey serves as a bridge for use of data from other sources. In particular, American Community Survey Same-Sex Householder data along with UCLA's Williams Institute data on LGBT population by county are combined with current living situation data taken from the LMSM-CI dataset. Results obtained from these sources are used as the prior distribution for Successive-Sampling Population Size Estimation (SS-PSE) - a method used to create a probability distribution over population sizes. The strength of our model is that it does not rely on estimates of community size taken during an RDS survey, which are prone to inaccuracies and not useful in other contexts. It allows unambiguous, useful data (such as living situation), to be used to estimate population sizes.Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications.Endovascular interventions are experiencing an important development. Despite many advantages of this type of intervention, catheter navigation is still a cause of difficulties or failure. Mechanical thrombectomy is one of these interventions where navigation difficulties are related to the ability to navigate the aortic arch and access the carotid. These difficulties are due to the selection of adequate catheters and guides for a specific anatomy and to the technical gesture to operate. The objective of this work is to propose a method to find similar endovascular navigation paths from pre-existing patients to support intervention in mechanical thrombectomy. For each patient, iso-centerlines of the aortic arch and supra-aortic trunks are extracted from pre-operative magnetic resonance angiography volume. A statistical shape model is computed from these vascular structure iso-centerlines. Euclidean distance between vectors of statistical shape model modes is used to compare endovascular navigation paths. A set of 6 patient cases was used to compute the statistical shape model. For validation, an additional set of 5 patient cases was considered to generate new iso-centerlines.Retrieval of closest iso-centerlines were correct in more than 95% of cases with the proposed method while this percentage goes down to 43% with Euclidean distance between 3D points of iso-centerlines.Clinical relevance-The presented method allows physicians to retrieve past navigation paths similar to a new one. Used in planning, this could allow to anticipate navigation difficulties in mechanical thrombectomy.The American Psychiatric Association has identified Internet gaming disorder (IGD) as a potential psychiatric disorder. Questionnaires are the main method to classify high-risk IGD (HIGD) and low-risk IGD (LIGD). However, the results obtained using questionnaires might be affected due to several factors. Flow can measure a person's state of concentration and cardiovascular signals can reflect the autonomic responses of a person. We propose to observe the cardiovascular responses and flow scores from the flow short scale of the HIGD and LIGD groups to assist questionnaires in IGD risk assessment. The preliminary study recruited 18 gamers from colleges. Games with the easy and hard levels were set to arouse desire for playing. The result showed that the flow scores of five HIGD participants were significantly lower compared with that of 13 LIGD participants. The stroke volume (SV) of the LIGD group during baseline (67.06 ± 11.61) was significantly greater that of (p less then 0.05) while playing the easy game (64.08 ± 10.37) and playing the hard game (63.70 ± 9.89). For the LIGD group, the cardiac output (CO) during baseline (5.28 ± 0.97) was significantly greater (p less then 0.01) than that of recovery (5.03 ± 0.83), and while playing the easy game (5.34 ± 0.98) it was significantly more than that during recovery (p less then 0.05). For the HIGD group, a significant difference in the heart rate, SV, and CO was not observed. The changes in cardiovascular responses of the LIGD group are greater than that of the HIGD group. Gamers with LIGD might have a higher susceptibility to the negative effect of playing video games, but gamers with HIGD might not. The finding of this study might help psychologists to estimate the IGD risk.Clinical Relevance- This study investigated the differences in the score of flow short scale, self-assessment manikin, challenge/skill, emotional questionnaire, and the changes in the cardiovascular responses between the HIGD and LIGD groups.