Search

While the adoption of digital twins among industrial companies is growing due to rapid developments in available solutions, the utilization of digital twins as a part of company operations and management is a new phenomenon. As a result, the long-term functionality of the digital-twin lifecycle is not well understood. Because of the small number of example cases, there is a lack of existing case data. This chapter offers both theoretical and empirical insights into the business-related risks that can arise during the different digital-twin lifecycle phases. The empirical information introduced here came from two Finnish multinational industrial companies that are planning to develop new service businesses based on digital twins. Post-print / Final draft

Oropharyngeal dysphagia is prevalent in several at-risk populations, including post-stroke patients, patients in intensive care and the elderly. Dysphagia contributes to longer hospital stays and poor outcomes, including pneumonia. Early identification of dysphagia is recommended as part of the evaluation of at-risk patients, but available bedside screening tools perform inconsistently. In this study, we developed algorithms to detect swallowing impairment using a novel accelerometer-based dysphagia detection system (DDS). A sample of 344 individuals was enrolled across seven sites in the United States. Dual-axis accelerometry signals were collected prospectively with simultaneous videofluoroscopy (VFSS) during swallows of liquid barium stimuli in thin, mildly, moderately and extremely thick consistencies. Signal processing classifiers were trained using linear discriminant analysis and 10,000 random training–test data splits. The primary objective was to develop an algorithm to detect impaired swallowing safety with thin liquids with an area under receiver operating characteristic curve (AUC) > 80% compared to the VFSS reference standard. Impaired swallowing safety was identified in 7.2% of the thin liquid boluses collected. At least one unsafe thin liquid bolus was found in 19.7% of participants, but participants did not exhibit impaired safety consistently. The DDS classifier algorithms identified participants with impaired thin liquid swallowing safety with a mean AUC of 81.5%, (sensitivity 90.4%, specificity 60.0%). Thicker consistencies were effective for reducing the frequency of penetration–aspiration. This DDS reached targeted performance goals in detecting impaired swallowing safety with thin liquids. Simultaneous measures by DDS and VFSS, as performed here, will be used for future validation studies. Electronic supplementary material The online version of this article (10.1007/s00455-018-09974-5) contains supplementary material, which is available to authorized users.

The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

The continuous demand for improved energy efficiency and reduced pollution has led the car manufacturers to focus on electric propulsion. Due to this, research activities concerning electric and hybrid vehicles have been increasing all over the world. For hybrid vehicle research and development, various modeling and simulation tools have been created as the prerequisites for computational research and development have been developed. This paper explores existing research from the past and presents recent developments in order to predict future trends in powertrain simulation tools. The results of the study indicate that developments in simulation tools have evolved from dedicated modeling and simulation tools for hybrid electric vehicles (HEVs) towards general multi-system simulation tools with increasingly detailed component models and accurate physics. The study results identified that progress in computing technology is steady, the development trend in computational R&D is moving towards large computational studies, and the application of advanced data analytics plays a strong role in hybrid powertrain R&D.

We evaluate the performance of an unobtrusive sleep monitoring system in the detection of the sleep apnea-hypopnea syndrome (SAHS). The proposed system is a pressure bed sensor (PBS) that incorporates multiple pressure sensors into a bed mattress to measure several physiological signals of the sleeping subject: respiration; heart rate; and body movements. An automatic algorithm is developed to calculate a respiratory event index (REI). The recordings of 24 patients with suspected sleep problems are analyzed, and the results are compared with the gold standard methods; first with manual scoring of polysomnography to calculate the apnea-hypopnea index (AHI), and second with automatic detection of REI from the respiratory inductive plethysmography belts. The correlation coefficient between AHI and REI from PBS is up to 0.93. Evaluating the ability of PBS in the diagnosis of pathologic (AHI $\ge 5$ ) and nonpathologic ( ${\rm AHI} ) subjects, we obtained a sensitivity, specificity, and accuracy of 100%, 92%, and 96%, respectively. To diagnose three levels of SAHS, mild, moderate, and severe, the Cohen’s kappa value is 0.76. These findings support that PBS recording could provide a simple and unobtrusive method for detection of SAHS in home monitoring.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG).Methods: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection.Results: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected.Conclusions: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.

In MRI-guided high-intensity focused ultrasound (MR-HIFU) therapy, temperature-dependent proton resonance frequency (PRF) shift is a key factor to quantify and visualize the spatial heating pattern in treated and surrounding tissue. However, during treatment, multiple scanner-related changes can impact the accuracy of the temperature measurements obtained with the PRF shift method and cause an over/under estimation of temperature, which can be a major safety issue for treatments involving real-time temperature control. Hence, it is necessary to apply corrections to ensure accurate temperature measurements during heating. Prior to image acquisition, the central MR frequency F0 can be measured to adjust the F0 of next image acquisition. After acquisition, corrections can be applied to the acquired images to remove scanner-related influences, most importantly phase drift. Different phase drift correction algorithms such as conventional and polynomial adaptive drift correction estimate the background phase by fitting a linear or polynomial to the image phase outside the treatment area and perform the correction accordingly. The goal of this study was to understand the performance of these algorithms for long heating durations as would be experienced during hyperthermia or transurethral HIFU (>20 minutes).

The authors go through the open source for product life cycle management (PLM) and the efforts done from communities such as the open source initiative. The characteristics of the open source solutions are highlighted as well. Next, the authors go through the requirements for PLM. This is an area where more attention has been given as the manufacturers are competing with the quality and life cycle costs of their products. Especially, the need of companies to try to get a strong position in providing services for their products and thus to make themselves less vulnerable to changes in the market has led to high interest in product life cycle simulation. The potential of applying semantic data management to solve these problems discussed in the light of recent developments. In addition, a basic roadmap is presented as to how the above-described problems could be tackled with open software solutions.

This paper proposes a new real-time method to measure the driver’s useful field of view (UFOV) while driving a car in ordinary traffic situations in an urban environment. This is called the real-time useful field of view (rUFOV) method to discriminate it from conventional UFOV measurement, which is typically performed offline and with laboratory equipment developed by Visual Awareness Inc. The proposed real-time method first tracks traffic objects that appear in the driver’s peripheral vision using a road video camera, checks the degree of the driver’s attention to these objects using a driver monitoring camera, and finally calculates the percentage reduction in the driver’s UFOV using a database acquired over an extended period of time. Preliminary results showed better performance than originally expected. The rUFOV method was then incorporated into a driving simulation environment to enable more precise measurement of the driver’s gaze angle. This enabled the performance of safer tests for identifying conditions under which mental load reduced the driver’s visual capabilities, thus increasing the possibility of hasty driving, as well as the incorporation of more accurate control parameters into simulation software for risky driving scenarios. Consequently, this paper proposes a new methodology for measuring the driver’s UFOV as a potential real-time driver support system with automatic intrusive HMI adaptation and immediate alarm functions. The evaluation was conducted in two phases. First, the system was tested in real traffic using typical vehicle equipment and technically worked with a performance level of 81%.In the second phase, more test runs were performed in the simulator environment, which enabled near accident scenarios to be created without risking traffic safety and it was also measured its reaction time..