R presents a new way to choose the base classifier for ensemble learning.Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator  Breast cancer is one of the most common malignant tumors in female ; it has Mammography and ultrasound are two most commonly used screening modalities for early detection and treatment of breast cancer. Mammography uses X-ray to noninvasively image the internal structure of human breast, which can be used to show the masses, calcifications, and any other suspicious area that could be identified as signs of breast cancer . HoweverMoreover, the radiologist's clinical experience and subjective judgment directly affect the accuracy of the diagnosis; the radiologist who lacks clinical experience may make an inaccurate diagnosis or miss a diagnosis. At the same time, the inherent high frequency noise and the shadow of medical images may also affect the accuracy of diagnosis. Therefore, a computer-aided diagnosis (CAD) using ultrasound with mammography which can improve the accuracy and specificity is needed in clinical application . The selR is proposed to choose the appropriate base classifier. Finally, the results of breast cancer diagnosis is obtained by the integrated classifier by majority vote. The extensive experimental results show that the ensemble classification method is efficient for breast cancer diagnosis in our database. The indicator R can choose the base classifiers with high accuracy and generalization.In this paper, we propose a selective ensemble classification method using mammography with ultrasound for breast cancer diagnosis. First, instead of the BI-RAD feature, we extract the texture and morphological features to obtain more information of the lesion's edge, shape, and texture on ultrasound and mammography. Then three classifiers, KNN, SVM, and Navies Bayes, which have been used to diagnose breast cancer by many researchers are employed for the ultrasound and mammography, respectively. To ensure the accuracy and the generalization of the base classifier, the new indicator The rest of the paper is organized as follows. In R. We choose the best integrated classifier and obtain the final result by majority vote to diagnose the breast cancer. The flow chart of the selective ensemble method has been shown in In this paper, we propose a selective ensemble classification method combining mammography with ultrasound images to diagnose breast cancer. Extracting the feature on ultrasound images and mammography images, respectively, we sampled these images with 10-fold cross-validation to train the base classifiers . Considering that integrating many of the classifiers may be better than integrating all of them, we obtain different integrated classifiers generated by three of all base classifiers and rank them by the indicator D : W) ratio, elliptic-normalized circumference (ENC), and the size of the lesion [D : W is the ratio of the depth and the width of the minimal circumscribed rectangle of the lesion. ENC is defined as the circumference ratio of the lesion and its equivalent ellipse, and it represents the irregular boundary of the lesions. The benign tumors will stop growing, while the malignant will not stop growing. The larger size is always with the malignant tumor.In this paper, we extract the minimum bounding rectangle around the lesion on ultrasound images and mammography images to calculate the gray-level cooccurrence matrix (GLCM) which can provide a method for generating texture features. GLCM are computed in four directions  with 8-pixel distance. The feature used for classification are: correlation, contrast, energy and entropy. Morphological features are beneficial to distinguishing between benign and malignant tumors on ultrasound images, so we extract three morphological features: depth-to-width  can reflect the diversity of the classifier [Nmn means the number of different classifier's results with m and n (m and n are 0 or 1). The smaller R is, the better the diversity the classifier will be. The different μ can impact the choice of the classifier :ACCassifier . As is sWe use six indicators to evaluate the performance of the system, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and the area under receiver operating characteristic curve (AUC).The accuracy is described as the ratio of the correct samples distinguished by the classifier to the total samples. The accuracy can be described as follows:The sensitivity is described as the ratio of the malignancy distinguished by the integrated classifier (TP) to the real malignancy (TP + FN). It can measure that how many times malignancy can be diagnosed. The specificity is described as the ratio of the benign tumor distinguished by the integrated classifier (TN) to the real benign tumor (TN + FP). It measures that how many times benign tumors was misdiagnosed as malignancy. The NPV can measure the diagnosis accuracy of benign tumor. The PPV can measure the diagnosis accuracy of malignancy tumor. These four indicators can be described as follows:In addition to the above, we also use the AUC which means the area of the ROC (receiver operating characteristic) curve to appraise the performance.To evaluate the effectiveness of our method, we do some experiments using the indicators mentioned above. The database used in this paper contains 142 medical images, including 71 ultrasound images and 71 mammography images. These images are acquired by 71 patients, 40 benign tumors and 31 malignant tumors. The programs are implemented with Matlab R2010b and WEKA, and data was entered into the computer on Intel 2.93 G dual-core processor with 3.29 G RAM.First, we compared the performance of single classifier with the integrated classifiers based on ultrasound images or mammography images, respectively. As is shown in In We propose the ensemble method based on multimodal images (mammography and ultrasound images) according to its complementary feature. In R to select the base classifier in our work. We ranked the different integrated classifiers with R and chose the top 10 integrated classifiers . Then we tested their performance to demonstrate the effectiveness of indicator R. The performance of the different integrated classifier is shown in R can effectively choose base classifiers. Considering that the sensitivity is more important in the clinical application and the integrated classifier C1 performs much better than the integrated classifier C2 in accuracy and sensitivity, we choose integrated classifier C1 which is ranked first by indicator R to diagnose breast cancer.We have proved that the integrated classifiers are better than single classifier. However, there are so many ways to integrate. We use the indicator In this paper, we integrated the classifier instead of integrating the feature to prevent the characteristic redundancy. To test the efficiency, we compared it with the different fusion methods. As is shown in GASEN (Genetic Algorithm based Selected Ensemble)  is the fR. Then we test our method in a multimodal database containing 71 breast ultrasound images and 71 mammography images. The selective ensemble method is efficient in diagnosing the breast cancer; we can obtain an accuracy of 88.73% and sensitivity of 97.06%. We also prove that the classifier-fusion method is better than the feature-fusion method in all indicators in our database. In our work, we just choose the simple feature. We will concentrate on the relationship of features ultrasound and mammography images in the future.In this paper, we propose a new selective ensemble method to diagnose breast cancer by combining ultrasound images with mammography images. When generating the integrated classifier, we choose the suitable base classifier using new indicator


These results are at least partially explained by the fact that force application to the rear block was clearly impaired in the affected legs of ASs.The purpose of this study was to explore the relationship between the forces applied to the starting blocks and the start performances (SPs) of amputee sprinters (ASs) and non-amputee sprinters (NASs). SPs of 154 male and female NASs  and 7 male ASs  with running specific prostheses (RSPs) were analysed during full-effort sprint starts using instrumented starting blocks that measured the applied forces in 3D. Using the NAS dataset and a combination of factor analysis and multiple regression techniques, we explored the relationship between force characteristics and SP . Start kinetics were subsequently compared between ASs and NASs who were matched based on their absolute 100 m PR and their 100 m PR relative to the world record in their starting class. In NASs, 86% of the variance in SP was shared with five latent factors on which measured parameters related to force application to the rear and front blocks and the respective push-off directions in the sagittal plane of motion were loaded. Mediolateral force application had little influence on SP. The SP of ASs was significantly reduced compared to that of NASs matched on the basis of relative 100-m PR (−33.8%;  Get ready, set, go! The start of the 100 m final is one of the most anticipated moments of any major athletics championship. The importance of an athlete’s start performance (SP) is inversely related to the length of the track event. It is therefore very important in the 100 m, less so in the 200 m and potentially most significant in 60 m indoor events. Despite the fact that, for a typical 100-m race, the start (including reaction time) only takes up about 5% of the total duration [Following Newton’s second law of motion, horizontal CoM acceleration requires net propulsive forces to be applied to the body of the athlete in the running direction. If force application is accompanied by motion of the sprinter, mechanical work is performed. Completing a given quantity of work in less time corresponds to an increase in average power generation over that period, so this parameter is considered an excellent descriptor of SP in sprinters . Muscle It is generally accepted that good acceleration performance in sprinting tasks requires highly efficient application of horizontal force , 9 in orThe aforementioned references indicate that acceleration performance can be improved by increasing the capacity of the musculoskeletal system to create power from a resting position. Furthermore, they show that the efficiency of horizontal force application might play an important role in improving acceleration during the start phase ,9. The aA deeper understanding of the mechanism underlying sprint start performance should improve sprint performance diagnostics and aid the design of technical drills and strength and conditioning programs for NASs and ASs.Therefore, in the present study we first explored potential latent factors . This NAS group comprised 103 males  and 51 females . The remainder of the study sample consisted of seven male ASs . Force signals were transformed from the local (tilted) starting-block reference system to a global coordinate system before further analysis was performed. The orientation of the global coordinate system was as follows: The x-axis pointed forward along the running surface , the y-axis pointed to the left along the same surface plane and the z-axis pointed vertically upwards. Mediolateral forces were described as follows: Positive values were used if the block reaction forces were in the direction of the contralateral leg, and negative values corresponded to the opposite situation. Because the dominant component of force was positive in the front leg and negative in the rear leg, maximal positive values in the front leg and minimal values in the rear leg were considered the maximum mediolateral forces applied to the blocks.To obtain the force data, we used a custom-made instrumented starting block consisting of a very stiff steel centre rail and separate block bases and force sensing units for each foot. Base units were available for different inclination angles and were screwed to the centre rail in order to provide a sufficiently stiff system for the force measurements, while enabling adjustment of start-block settings to those used for training and competition. Small custom-made force platforms, each including four piezo-type 3D force transducers , were screwed onto the tops of the block bases for force measurements . Analog NAHBP) [TBlock):The following parameters were extracted for analysis: Overall start performance was described using normalized average horizontal (in the running direction) block power (NAHBP) . AverageTBlock, time from first reaction to block clearance) and CoM velocity at block clearance . Body mass (m) included the mass of the prosthetic parts in ASs. As the initial velocity (Vi) is zero in the set position of the sprint start, the formula for the calculation of average horizontal block power (We measured block time (m) and body height (h) in order to achieve a dimensionless normalized average horizontal block power :NAHBP=Inserting In contrast to the approach taken by Bezodis et al. , body heΛ in the factor-analysis model,x is a vector of observed force parameters, μ is a constant vector of means, Λ is a matrix of factor loadings, f is a vector of independent, standardized common factors, and e is a vector of independent specific factors. To identify the number of factors to extract for further analysis, the Kaiser criterion [t tests. To address potential problems due to unequal variances, Satterthwaite's approximation for the effective degrees of freedom was used [α, was set at 0.05. Due to the low size in the AS sample and related statistical power, we also calculated effect sizes (Cohens d) in order to allow for an estimation of the strength of an observed difference [Each athlete performed at least three full-effort sprint starts over a distance of 20 m. The best start (based on NAHBP) was selected for further analysis. To identify potential latent factors affecting SP, we performed an exploratory factor analysis (EFA) using selected force parameters. EFA is a statistical procedure used to analyze variability among measured correlated variables with respect to a potentially lower number of unobserved (unmeasured) or latent variables, which are termed factors. For example, it could be the case that variations in a great number of observed sprint-start kinetic parameters are actually just the result of variability in a much smaller group of underlying parameters (factors) that represent more fundamental sprint-start abilities. EFA is aimed at finding measured parameters that vary as a group in response to latent variables. In our case, the dataset representing the observed variables included average and peak forces in all directions and parameters describing the push-off direction . Using Matlab’s  built-in “factoran” function, we calculated the maximum likelihood estimate (MLE) of the factor loadings matrix riterion  were appriterion  in orderriterion . Using friterion . Furtherwas used . The sigfference . Effect fference .SP shared 42% of its variance with 100 m PR in the NAS group , Fig 2. The factor analysis model revealed that, based on both the Kaiser criterion  and by visual inspection of the scree plot , the firMediolateral force application in the front and rear blocks and the corresponding mediolateral push-off directions loaded high on factors 2 and 5 , respectively . Paramet2 values as the complete model including all seven factors  reduction of 17.7% was observed (Start performance was 33.8% lower (p<0.001) in ASs versus NASs matched with respect to relative 100 m PR . When maobserved . Force aobserved , while eobserved .Differences in force-application patterns were clearly seen when the force waveforms of the best (with respect to 100 m PR) NAS and the best unilateral transfemoral amputee were compared .When comparing athletes with different amputation levels, it was seen that athletes with more proximally (higher) located amputations exerted less force with their affected limb than athletes with a more distal (lower) amputation or a bilateral amputation . NonetheThe first task of the present study was to investigate the presence of a potentially underlying factor structure for ground-force application during the sprint start. The present NAS dataset was very well suited for such an analysis as it was sufficiently large and represented 100 m sprinters from all relevant performance categories, including even the highest level of performance. Although seven factors seem to be a good choice for proper representation of the variability contained in the ground-force application data set based on the Kaiser and elbow criteria, the results of the present study indicate that only five factors are needed to explain 86% of the variance observed in SP. The remaining two factors, which are related to force application in the mediolateral direction, did not significantly improve the predictive power of any of our multiple linear regression models. This suggests that no performance benefit would accrue from modifying a non-amputee’s starting-technique to minimize mediolateral-force application and achieve a straighter push-off in the forward direction.Using the scores from the above explorative factor analysis as predictors in a multiple regression analysis offers two main advantages over the more common practice of implementing all directly measured parameters in the multiple regression analysis: On the one hand, when estimating the coefficients of the regression model, it avoids potential problems that can result from multicollinearity among the predictor variables . In the In the present study, the factor representing force application to the rear block showed the highest estimated coefficient , followed by those representing push-off direction in the sagittal plane  and force application to the front block . The 95% confidence intervals of factor 1 overlap minimally with those of the other factors, indicating that it is potentially more important for SP than the other factors. This highlights the importance of high force application to the blocks in the sprint start. High coefficient estimates were also found for factors representing the amplitude and direction of force application to the front block. With respect to starting technique, this indicates that a high average force needs to be applied to both blocks in the horizontal direction. It is interesting to note that factors relating to force-application amplitude and direction were of similar magnitude in the front block, whereas in the rear block, the coefficient estimate for the direction factor  was 4-fold lower than the corresponding amplitude coefficient . This result indicates that forces at the rear block need to be maximised, but ensuring that they are well aligned to the running direction is less important. In contrast, at the front block, both force amplitude and direction were of similar importance.Another interesting result of the present study is the fact that two factors (4 and 6) were related to the force amplitude at the front block. As these factors are independent of each other, they may represent two different targets for improving SP. Looking at If instrumented starting blocks are available for performance diagnostics or for biofeedback training, we recommend use of at least the average resultant forces in the front and the rear blocks, as well as the ratio of the anterior and resultant impulses as criterion parameters, since they were highly loaded on most of the factors important for prediction of SP. As mediolateral-force application parameters did not improve the prediction of starting performance, they appear to be of less importance for these tasks. The design of instrumented starting blocks for performance diagnostics or biofeedback might therefore be simplified by using only 2D force sensors to measure forces in the running and vertical directions.Once our exploratory analysis of the NAS data was complete, we compared the start performance of ASs and NASs. All unilateral ASs preferred to place their affected leg on the rear block, which is consistent with observations from video recordings summarized in Taboga et al. ; 86% of Comparing ASs and NASs that were matched with respect to their relative (to corresponding world record) 100 m PRs, SP was reduced by 33.8% for the amputee athletes, and this was associated with an average increase in block time of 0.08 s. Interestingly, average resultant forces applied to the front blocks were not lower in ASs, but they were applied in a more vertical direction, which has been shown to be detrimental for acceleration performance . Furtherp value of 0.08, this reduction was not sufficient to be considered statistically significant. Both groups of athletes were similar with respect to their overall 100 m race performance (100 m PR). From these results, it can be concluded that during the race phases where speed was constant, ASs must have performed better compared to their non-amputee counterparts. We suggest two possible explanations for this. Firstly, the level of professionalism may be significantly higher for the particular ASs in this study. Some of these athletes compete at the very highest level of their sport, in the 100 m, 200 m and long jump, and this is reflected in the significant difference for relative 100 m PRs . These amputee athletes may simply spend, on average, more time and effort on training and active recovery than the matching NASs. The second possible explanation is that sprint-specific prostheses, though inferior during the start phase, performed better in replacing the functionality of biological limbs during the maximum-constant-speed phase of the race by enabling the spring-like energy exchange in the lower limbs during ground contact [When the two groups were matched with respect to their absolute 100 m PRs, start performance was again reduced, this time by 17.7%—however, with a  contact . Additio contact . During  contact . In ampuNevertheless, the interaction between passive prosthetics and the remaining limb anatomy is challenging from a coordinative perspective, not least on account of the missing sensory input from muscle spindles, Golgi tendon organs and other biological sensors at distal locations on the leg. Furthermore, it has been argued that maximum sprint velocity can be impaired by limitations that RSPs impose on ground-force application and leg stiffness , 27.In summary, the results of the present study emphasize the importance of high average force application to both rear and front blocks. In addition, the forces should be applied as horizontally as possible, in the direction of forward motion. The avoidance of high mediolateral forces had no significant effect on start performance in non-amputee sprinters. These features of successful push-off from the starting blocks are consistent with recently published studies of world-class athletes . Force a


Acquired Brain Injuries (ABIs) can result in multiple detrimental cognitive effects, such as reduced memory capability, concentration, and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of ABIs and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting, but recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy.The aim of this research was to investigate how cognitive fatigue can be accurately assessed in situ, during the quotidian activities of life. It was hypothesized that this assessment could be achieved through the use of mobile assistive technology to assess working memory, sustained attention, information processing speed, reaction time, and cognitive throughput.The study used a bespoke smartphone app to track daily cognitive performance, in order to assess potential levels of cognitive fatigue. Twenty-one participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone app delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task (PVT) to measure sustained attention, information processing speed, and reaction time; and (3) a Mental Arithmetic Test to measure cognitive throughput. A smartphone-optimized version of the Mental Fatigue Scale (MFS) self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer-reviewed studies.P<.008). Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the PVT was a significant predictor of fatigue . Higher scores on the MFS were related to increases in reaction time during our mobile variant of the PVT.The most highly correlated results were from the PVT, which showed a positive correlation with those from the prevalidated MFS, measuring 0.342 (The results show that the PVT mobile cognitive test developed for this study could be used as a valid and reliable method for measuring cognitive fatigue in situ. This test would remove the subjectivity associated with established self-assessment approaches and the need for assessments to be performed in a medical setting. Based on our findings, future work could explore delivering a small set of tests with increased duration to further improve measurement reliability. Moreover, as the smartphone assessment tool can be used as part of everyday life, additional sources of data relating to physiological, psychological, and environmental context could be included within the analysis to improve the nature and precision of the assessment process. Cognitive fatigue can be caused by multiple different conditions, including Acquired Brain Injuries (ABIs) , ParkinsEvaluation of cognitive fatigue has traditionally been performed using self-assessment in the form of easy-to-comprehend questionnaires that are often delivered within a clinical setting . AccordiMore recently, research into computerized cognitive fatigue assessment has sought to adopt cognitive testing methods by either adapting paper-based methods or repurposing cognitive testing batteries -17. WhilOriginally designed for a static desktop computer-based evaluation, PVT has since been modified for use on a mobile-based platform  and How can cognitive fatigue be accurately assessed through the use of a mobile assistive technology?”. As such, the study sought to evaluate the correlations between subjective and objective measures of cognitive fatigue, with a future view to supplement traditional clinical evaluations with in situ cognitive assessment. From this approach, the following hypotheses were formulated: (1) a correlation will be observed between objective cognitive testing performance and the subjective measure reported by the MFS; (2) participants who are cognitively fatigued would exhibit a reduced level of performance in Spatial Span, PVT, and Mental Arithmetic Tests; and (3) accurate cognitive fatigue assessment can be carried out on a mobile app for use in everyday life.The study presented in this paper aimed to address the research question, “ As highlighted, the assessment of cognitive fatigue commonly takes place within a clinical environment, which does not allow for accurate in situ assessment. This study primarily explored the adaptation of cognitive fatigue tests for delivery using a smartphone. During the study, the validity of the smartphone-based tests was compared to self-reported cognitive fatigue levels, as measured using the MFS. Accordingly, the MFS version employed had to be adapted to allow for delivery on a smartphone. Three cognitive tests were chosen from those identified in the literature. The Spatial Span Test was selected for inclusion to assess working memory. The PVT was also selected, as it has been proven as a cognitive fatigue evaluation method ; moreoveExpert Review informed the development and design process to establish a Prototype app. In turn, this Prototype was then used in a Pilot Study that focused on user-centered design, to test our three hypothesized assumptions and to refine the protocol for eventual deployment during the target study. This process permitted the design and development of the app to be informed by clinical theory and practice, as well as commercial design theory and participant feedback. A multi-disciplinary, iterative approach  is crucial. Through Expert Review, the order of the selected tests was based on a combination of the perceived level of difficulty and expected level of participant enjoyment. These considerations subsequently lead to potentially less-engaging tests being sequenced earlier in the workflow of the app, whereas more engaging tests were sequenced later. Accordingly, the cognitive tests presented to participants would become increasingly stimulating over the course of a session. As a result, the ordering of the tests presented by the app was: MFS, Spatial Span Test, PVT, and Mental Arithmetic Test. In addition, the Expert Review also helped to ensure that any changes made to the MFS, specifically its redesign for optimal presentation on a smartphone screen, did not compromise the validity of the corresponding results.Following the initial Expert Review stage, a Pilot Study was delivered to a small set of participants (n=5), using a first-iteration prototype app. This stage facilitated an evaluation of the efficacy of each of the selected cognitive tests to help ensure that they would challenge participants sufficiently, and facilitate collection of a dataset with appropriate variability to permit subsequent analyses. The Pilot Study also provided an opportunity to gain feedback on information design and visual design choices made during the Expert Review, which might affect the usability of the app.The iterative nature of the design process allowed for further Expert Review to inform a response to findings from the Pilot Study, which helped to further refine and optimize the design of the app and the planned larger-scale study. A notable instance of this process is the design of the Spatial Span Test, which was revised from a 5x5 grid to a 4x4 grid layout, as the original design proved difficult to accurately tap when displayed on a smartphone screen. Additionally, a countdown timer and progress bar were added to inform participants of both the remaining time during the test and their ongoing progress.The design of the PVT was also revised to introduce a randomly positioned stimulus, to prevent participants from preempting a response. Immediate visual feedback was also incorporated to encourage concentration and participation during the test. In terms of the Mental Arithmetic Test, initial concerns regarding the layout of the on-screen number keypad used for participant’s responses was validated, with a telephone style layout proving acceptable to participants. Moreover, changes to the MFS element of the app required much more care, as the purpose of including it was to provide an established cognitive fatigue assessment tool to help evaluate how effective the mobile cognitive tests were at assessing cognitive fatigue. To maintain the validity of the MFS, it was important that it differed as little as possible from the original paper-based questionnaire. However, the presentation of each question as a separate element marks a departure from the original paper-based version; this change was made in response to feedback from the design process that found a single continuous onscreen set of questions to be awkward from a user-interaction viewpoint.Deploy within the target study. At the start of the study, guidance was given to each participant on the nature of the study and informed consent was obtained, for the purpose of conducting the study in an ethical manner. Subsequently, a brief tutorial on how to use the app was presented. Once the study had commenced, participants received a push notification daily at 3 o’clock p.m. as a reminder to carry out a session with the app. Upon launching the app, the participant was initially presented with brief instructions regarding the purpose and usage of the app, which was subsequently followed by delivery of the MFS and the set of cognitive tests in the predefined order: (1) MFS; (2) Spatial Span Test; (3) PVT; and (4) Mental Arithmetic Test. Completion of both the MFS and set of cognitive tests took approximately 5 minutes in total. Upon completion, the data collected was initially stored on the mobile device, before subsequent transmission to an online database. The set of tests presented to participants are illustrated in Upon completion of different iterations of Expert Review, Pilot Study, and subsequent iterations of Prototype development, the smartphone app was ready to As shown on the left side of The second test presented was the Spatial Span Test, which required each participant to recreate a sequence of flashing boxes that appeared on the screen one after the other in a grid formation. If the participant correctly repeated the presented sequence, they were then presented with a new sequence that contained one additional box. Conversely, if the participant entered an incorrect sequence, then the next sequence was one box shorter, with a minimum sequence of three boxes being presented. The use of adaptive difficulty in this manner was intended to help maintain engagement regardless of the participant’s level of performance. The Spatial Span Test lasted for a total of 90 seconds, during which time participants attempted to complete as many sequences as possible.Upon completion, the PVT was presente: the mobile implementation of PVT required the participant to attempt to achieve the quickest reaction times possible in response to the presentation of onscreen stimuli 20 times; each time a large block of color was randomly displayed on one half of the smartphone screen.The final test presented was the Mental Arithmetic Test, as illustrated on the right side of Each specific test captured a range of different data points to assess relative performance. These data included correct and incorrect responses and time it took to respond during each task. Data that was collected via the app is detailed in Strongly Agree to Strongly Disagree. Additionally, participants were asked to freely comment on any aspect of the app or study to provide further feedback.Upon conclusion of the study, all participants were invited to provide feedback on the smartphone app using an online version of the System Usability Scale (SUS) . For theParticipants (n=21) were recruited within Ulster University to undertake the study over a two-week period. Participants with no prior brain injuries were explicitly chosen, as it was considered unethical to test a newly developed method on a clinical population without first understanding how it might be of benefit. Moreover, the experience of cognitive fatigue, while a characteristic of ABI, is not limited to the condition. The mean age of participants recruited was 22 years (standard deviation=4). In addition, participants were required to own an iPhone to ensure that they were familiar with using iOS-based smartphone apps, which removed the need for additional training in device use prior to undertaking the study. Participants were required to use the app once daily to self-assess their cognitive fatigue levels. During the study, 81 individual testing instances were recorded, resulting in an overall daily adherence by participants of 28%.Data were analyzed using IBM SPSS version 22. Descriptive statistics were used to provide information on average reaction time, MFS questionnaire total score, total number of timely reactions, total number of correct Spatial Span sequences, total number of correct answers to Mental Arithmetic questions, and longest sequence achieved in the Spatial Span Test . PearsonTo identify outliers of extreme and incorrect testing instances, data were analyzed using box plots. Analysis of outliers used the standard measure of 1.5 times the interquartile range to find outliers that were viewed to be too far from the central values to be reasonable. A likely cause of outliers is inaccurate self-assessment , in conjunction with a simplistic pattern of responses observed . Removal of outlying instances of this type helped to ensure only reliably accurate tests were included during analysis.Overall, 81 individual testing instances were recorded by the participant population, resulting in a daily adherence by participants of 28%. Daily reminder notifications resulted in a participant adherence rate of 23% within the first two hours of receiving the notification; this finding indicates that a large proportion of app usage instances occurred due to the daily reminders.P=.008). By comparison, the average Spatial Span sequences correct had a correlation of -0.141 (P=.209), and the total Mental Arithmetic questions correct had a correlation of -0.016 (P=.884). All correlations are detailed in Initial analysis of the results obtained indicated the average reaction time during the PVT had the strongest correlation with the MFS for evaluating cognitive fatigue, measuring 0.342 , which explained 8% (adjusted R-square=.078) of the variance observed in fatigue scores. The only statistically significant variable to predict fatigue was PVT average reaction time.The best correlation to the MFS was from the average reaction times in the PVT. The significance of this correlation shows that the PVT may be a valid way to assess cognitive fatigue as a replacement for self-reporting. Average reaction time, total number of correct reactions, total number of correct Spatial Span sequences, and total number of correct Mental Arithmetic questions were entered into a multiple linear regression model to determine which factors were predictive of fatigue see . CorrespSelf-reported cognitive fatigue measured by the MFS was also shown to correlate with a slower average reaction time, which supports the hypothesis that information processing speed is a predictor of fatigue. From morning, afternoon, evening). Analysis of the results obtained from the MFS demonstrates that the morning epoch produced the highest self-reported levels of cognitive fatigue, which were 30.18% higher than during the afternoon epoch, and 28.21% higher than during the evening epoch. Although there was no statistical significance observed, there is an interesting correlation with the metrics outlined in As previously mentioned, results data were grouped into three epochs throughout the day , analysis indicates that only the PVT is significant enough to make claims on its validity. Subsequently, both the Spatial Span Test and Mental Arithmetic Test do not provide a strong correlation with the MFS for measuring cognitive fatigue. Although a correlation may be observed from all three of the tests as hypothesized, currently only the PVT has the potential to accurately assess cognitive fatigue using a mobile device, as it is able to determine the current level of mental fatigue concordant with the levels reported using the MFS. Furthermore, PVT is less demanding for participants to undertake than the other cognitive tests. Feedback from a usability viewpoint also suggested that the PVT was considered by participants to be more engaging than the MFS. From a participant perspective, assessment of cognitive fatigue is feasible via a smartphone app; however, adherence to regular testing is crucial to gaining an understanding of the condition over time.Adaptation of the smartphone app to increase the overall time on the cognitive tests could potentially lead to more accurate evaluation results across all tests, although this is only possible if participants are willing to take part in a longer daily task. Tailoring time-on-task to promote continued adherence, while also collecting sufficient data to accurately assess the condition, is a critical balance that requires further research. Such studies will enable a greater understanding of the testing duration required to permit accurate mobile-based cognitive fatigue evaluation. Future work may benefit from increased time-on-task for each of the three cognitive tests.When considering the scores of the PVT from individual participants, it can be observed that there was no improvement in scores over time. It is important that the participants find the test equally taxing over time, so that the same indicators of fatigue are present, rather than a participant learning from previous sessions and becoming increasingly expert in undertaking the assessment. Such learning would consequentially mask any underlying cognitive fatigue.The approach of interpreting fatigue using a mobile device has been demonstrated to have validity. However, a number of improvements could be made to the process, in addition to changes to the specific data that is collected. Such changes could include collecting data on environmental factors such as the quantity and quality of sleep, daily exercise levels, current location and social environment, and current emotional status. Based on the study performed, participant feedback indicated that carrying out the MFS was one of the least enjoyable parts of the overall process, which potentially lead to a reduction in engagement. The use of PVT has been shown to provide a similar capability in the assessment of cognitive fatigue, meaning that future work can potentially exclude the use of MFS. Subsequently, this exclusion may further increase user participation rates, which in turn may potentially increase the accuracy of the cognitive fatigue evaluation. An abridged variation of PVT was employed within the smartphone app; however, it is anticipated that a higher degree of accuracy could be achieved by using a longer test session. Accordingly, future work should increase the length of the PVT utilized, which could produce a more precise indicator of fatigue.A limitation of the study is that daily participation was not enforced. This enforcement could have increased the amount of data obtained, thereby improving the confidence of the findings. Given the real-world approach employed throughout the study, the only available (and appropriate) participation encouragement mechanism was seen to be mobile device notifications. Future work will aim to improve participation rates, in conjunction with increasing the amount of data collected to potentially obtain a more accurate assessment of daily fatigue levels. Furthermore, all cognitive test metrics that were employed alongside the MFS were constrained to a relatively short timeframe, in order to aid participation rates. However, this limitation may have had an adverse impact on the effectiveness of the tests in identifying small discrepancies in performance. Other limitations of the current study include the short duration of the overall study; future work will seek to address all of these limitations.The validity of assessing fatigue using both the Spatial Span Test and Mental Arithmetic Test has been proven to be effective by Van Dongen et al  and JohaWork carried out by Thomson et al  demonstrHowever, in terms of the efficacy of mobile-based self-assessment, Swendeman et al  carried touchdown, swipe, and touch-up, and concluded that participants preferred touchdown due to it similarities to a button press [One key concern with cognitive tests (such as PVT) that have been previously adapted for a mobile platform  is generon press . The stuon press . Kay andPrevious studies have confirmed the use of the MFS and PVT as a feasible way to assess cognitive fatigue in a clinical setting. This study demonstrated that smartphone-based adaptations of these proven methods are internally compatible methods of assessing cognitive fatigue in situ. The smartphone app presented in this research provides a potentially effective tool for the individual evaluation of cognitive fatigue levels in situations where formal intervention and assessment approaches are neither feasible nor available. Furthermore, the use of smartphone app-based fatigue assessment permits evaluation to be carried out on a continual daily basis.All three of the cognitive tests employed within the smartphone app produced positive participant feedback, with some participants even indicating that they would like personal scores, as it would further encourage them to participate more frequently. Consequently, by introducing a competitive aspect to the cognitive tests, participant effort and daily participation rates could potentially be improved. Future work may additionally permit the provision of real-time data to relevant medical professionals, so effective and timely interventions can be arranged if extreme fatigue becomes apparent. Correspondingly, there exists three main areas that in situ fatigue assessment could benefit from sensor data and contextual factors: (1) to improve notifications so that daily participation can be increased; (2) to provide additional data that may give a more precise indication of the occurrence of cognitive fatigue; and (3) to track a participant’s daily activity and advise them on appropriate steps to further combat cognitive fatigue.Based on the three episodic epochs, results from both the MFS and PVT indicated that the morning epoch produced higher levels of fatigue. Accordingly, this knowledge could be employed on an individual basis to help tailor the time of delivery of the test session, to pinpoint higher fatigue levels throughout the day. Adaptation of notifications (based on collected data over time) would further facilitate assessment when fatigue is usually at its highest. Correspondingly, identifying the time of day when fatigue levels are high offers an increased ability to prevent mental fatigue by preempting its arrival and suggesting steps to take to reduce its severity. Assessing the time of an individual’s participation could help inform notifications for the most likely time of adherence.


Inertial navigation system (INS)/Doppler velocity log (DVL) integration is the most common navigation solution for underwater vehicles. Due to the complex underwater environment, the velocity information provided by DVL always contains some errors. To improve navigation accuracy, zero velocity update (ZUPT) technology is considered, which is an effective algorithm for land vehicles to mitigate the navigation error during the pure INS mode. However, in contrast to ground vehicles, the ZUPT solution cannot be used directly for underwater vehicles because of the existence of the water current. In order to leverage the strengths of the ZUPT method and the INS/DVL solution, an interactive multiple model (IMM)-aided ZUPT methodology for the INS/DVL-integrated underwater navigation system is proposed. Both the INS/DVL and INS/ZUPT models are constructed and operated in parallel, with weights calculated according to their innovations and innovation covariance matrices. Simulations are conducted to evaluate the proposed algorithm. The results indicate that the IMM-aided ZUPT solution outperforms both the INS/DVL solution and the INS/ZUPT solution in the underwater environment, which can properly distinguish between the ZUPT and non-ZUPT conditions. In addition, during DVL outage, the effectiveness of the proposed algorithm is also verified. Currently, precise positioning and navigation technology still remains a challenge for underwater vehicles due to blockages of the global positioning system (GPS). With complementary characteristics, the inertial navigation system (INS) and Doppler velocity log (DVL) are the most common navigation solutions for accomplishing underwater positioning tasks . As a seThere are various errors in the INS/DVL integrated navigation system, such as installation error and scale factor error ,5. NormaIn order to reduce the negative effect caused by the DVL velocity error, in this work zero velocity update (ZUPT) methodology is introduced to the INS/DVL-integrated navigation system to restrict the vehicle’s lateral velocity error. ZUPT is one of the most popular solutions in land vehicles navigation systems, and well utilizes the characteristics of the land vehicle motion. Static ZUPT was first raised to utilize the zero velocity condition during every stop to control the navigation error growth. The curve fitting and KF are two main methods for applying the static ZUPT, while the latter shows a higher degree of navigation accuracy . When thHowever, for the INS/DVL-integrated navigation system, where errors can be easily involved in the DVL velocity information due to the complex underwater environment, the ZUPT solution should be further explored to mitigate the lateral velocity and positioning errors caused by the DVL velocity errors. When the water current remains still or the underwater vehicle travels along the current, the lateral velocity of the vehicle should always be zero. Using the lateral constraint to replace the observation vector calculated by the DVL, the navigation performance can be improved because a more accurate observation vector can be constructed to restrict the accumulation of the positioning error.To employ the ZUPT technology properly, its zero velocity condition needs to be correctly identified. In most research, the zero velocity condition is indicated by the inertial measurement units (IMUs). The mean value and standard deviation of IMUs during the static period are calculated to provide the threshold to judge the zero velocity condition . An adapThus, to balance the dynamic ZUPT methodology and INS/DVL integration simultaneously, and to identify the ZUPT condition to realize the smooth switching process properly, an interactive multiple model (IMM)-aided ZUPT methodology for INS/DVL-integrated navigation system is proposed. Two integration models, which are denoted as the INS/ZUPT model and INS/DVL model, are established and updated simultaneously to represent different driving statuses. Given the Markov chain process for the transition between different models, the IMM algorithm can easily identify the current driving status and make a trade-off between the estimation performances of the two models according to their innovations and innovation covariance matrices. Compared to the traditional single INS/DVL model-based solution, the IMM-aided algorithm makes it possible to employ ZUPT methodology to deal with the zero velocity condition and the INS/DVL method to deal with the non-zero velocity condition, which may improve the navigation performance when the underwater vehicle travels under various patterns. Moreover, the zero velocity detector is no longer needed to identify whether the whole system can be operated under the ZUPT conditions or not, as it is self-contained in the IMM model probability update process.The IMM algorithm was first widely applied in the target tracking area. To better predict the position of the target, the constant location (CL), constant velocity (CV), constant acceleration (CA) and constant turn rate (CT) models are fused by IMM to cover all of the possible motions ,21. In rThe rest of this paper is organized as follows. In this work, the dynamic ZUPT is explored, which provides the lateral and vertical velocity restrictions for the moving vehicles. When the water current remains still, it is highly possible that the underwater vehicle travels along the body-frame-forward direction, which is similar to land vehicles. Although DVL scale factor error can be estimated by INS/DVL integration, there are still errors left due to the complex underwater environment. The velocity information provided by DVL may contain biases, which would contaminate the navigation accuracy of the system. However, as the underwater vehicles are likely to be under simple motion for the most of the time, dynamic ZUPT is a very practical and effective method for reducing the accumulation of the lateral and vertical positioning errors. In this section, INS mechanization is described and its error model is derived. Then, INS/ZUPT is integrated by KF, where zero velocity in lateral and vertical directions of the vehicle’s body frame is introduced to the integration mechanization.i-frame: Earth-centered initially-fixed orthogonal reference frame; b-frame: Orthogonal reference frame aligned with the inertial measurement unit (IMU) axes; First, define the coordinate frames employed in this work: Taking the local geographical frame as the navigation frame, the differential equations of the attitude, velocity and position are :(1)C˙bn=h are updated according to the velocity, where The relationship between Let Then, the error model of the INS can be derived :(12)ϕ˙n=A 15-state KF is employed to complete the integration. The state vector The process model and observation model are:When the dynamic ZUPT condition is satisfied, the lateral and vertical velocity of the body frame should be zero, and the longitudinal velocity is provided by DVL. The observation vector can be calculated as :(19)Z=VIThe relationship between the observation vector and the state vector is derived as follows:The corresponding observation matrix can be obtained:Equation (18) is transformed into the discrete time formula:The update and prediction processes are illustrated as follows :(25)X^k,Compared to the land vehicle, the underwater vehicle’s motion is easily influenced by the water current. When the surrounding water current remains still, applying the dynamic ZUPT solution is a proper way to mitigate the lateral and vertical velocity errors caused by the DVL velocity errors. However, when a water current exists, it is quite easy for the underwater vehicle to obtain a lateral or vertical velocity, where the ZUPT solution no longer works. Thus, the IMM algorithm is introduced into the underwater INS/DVL-integrated navigation system to ensure the capability of both INS/ZUPT and INS/DVL models in their applicable environment. Meanwhile, when DVL is unavailable, where the system is operating under the pure INS mode, the IMM algorithm is also explored to detect the ZUPT condition and make a balance between the ZUPT mode and pure INS mode.The essence of the IMM algorithm is to obtain a weighted sum of the estimations of the ZUPT and non-ZUPT filters, which are updated in parallel. After calculating the innovation and innovation covariance matrix of each model, the IMM algorithm is able to choose the optimal model to describe the current driving situation. In prior research, the ZUPT detector was needed to identify the ZUPT driving condition, where the IMU sensor data is carefully analyzed. In contrast, the proposed IMM-aided ZUPT solution employs the innovation and innovation covariance information to autonomously identify the optimal model in the current moment, which avoids the complex identification indicated by IMU sensors and reduces the time delay.Based on the Markovian transition probability between different models, the interactive process of the individual filters can be described in four parts .(1) Interaction:i to model j. j, and Each model has its own filter. Given the Markov model transition probability and model probability calculated at the end of the previous cycle, the state vector and the estimate covariance of each filter are updated according to the model transition probability:(2) Model Filtering:j.Given the interacted state vector and covariance matrix of each model, the traditional KF is operated in individual filters, which are referred to the Equations (25)–(29). The innovations and their covariance matrices should be recorded to update the model probability in the next step, which are:(3) Model Probability Update:m is the dimension of the observation vector. The model probability is updated according to the different The model probability is updated according to the innovations and innovation covariance matrices. Assuming the innovation obeys the Gaussian distribution with a mean value of 0 and a variance of (4) Output Combination:Given the newly updated weight, the outputs of individual filters are integrated according to their different model probability.Simulations are conducted to evaluate the proposed IMM-aided ZUPT methodology for underwater INS/DVL-integrated navigation system. Different from the land vehicles, whose lateral and vertical velocities can be always set zero, the validity of the ZUPT solution of the underwater vehicles should be researched under the complex water current environment. The traditional underwater navigation system has no knowledge about whether there is a water current or not. Thus, the ZUPT solution, which can be easily applied to the land vehicles, cannot be directly employed in the underwater environment. The proposed IMM-aided ZUPT solution shows its advantages, which works whenever water current exists or not. First, compared to INS/DVL integration, the effectiveness of the INS/ZUPT solution is investigated when the underwater vehicle is driving under the no water current environment. Then, when the water current exists, whether the proposed algorithm can identify the non-ZUPT condition and navigate with the traditional INS/DVL solution or not is explored. Besides, the capability of the real time model estimation of the proposed algorithm when the vehicle travels under different water current conditions is evaluated through the experiments. In addition, when DVL is unavailable, the effectiveness of the prediction of whether the system should work under the pure INS mode or ZUPT mode is studied.The whole experiment lasts for 1500 s, and consists of acceleration, deceleration, uniform motion and turning motion. The integration experiment, where DVL is available, is operated in the first 900 s. When DVL is unavailable after 900 s, the performance of the proposed algorithm is also evaluated and compared with the pure INS solution. The initial position of the carrier is set as 32al tests . Thus, fThe blue line in It can be seen that the attitude error is below 0.005From 0 s to 250 s, where no water current exists, the attitude performance of the INS/ZUPT solution is similar to that of the INS/DVL solution. However, as the lateral velocity is bounded by the ZUPT condition, the horizontal velocity error of the INS/ZUPT solution is smaller, at around 0.0313 m/s. As a result, the horizontal position error of the INS/ZUPT solution is 7.345 m at 250 s, while that of the INS/DVL solution reaches 9.108 m. Thus, the INS/ZUPT outperforms the INS/DVL solution under the no water current environment. However, when the water current occurs at 250 s, the lateral velocity of the vehicle is no longer zero. The erroneous restriction on the lateral velocity of the vehicle collapses the whole system, where the attitude, velocity and position errors run within 400 s up to 4The attitude, velocity and position errors of the proposed algorithm are shown in It can be concluded that the INS/ZUPT method can mitigate the velocity and position errors when the underwater vehicle travels with no water current, but fail to navigate when the water current exists. The proposed IMM-aided ZUPT methodology outperforms both the INS/DVL and INS/ZUPT methods, which can easily identify the ZUPT and non-ZUPT condition and make a leverage between the two models. By analyzing the innovations and innovation covariance matrices, the IMM algorithm can automatically allocate the corresponding weights for the two models.Furthermore, when DVL is unavailable, the capability of the proposed algorithm is also evaluated. Normally, the IMM algorithm is only used in the integrated system. In this work, the IMM algorithm is also employed during DVL outage to identify the proper solution for the standalone INS. The whole DVL outage period starts from 900 s and ends at 1500 s. The proposed IMM-aided ZUPT algorithm is utilized to navigate from 0 s to 900 s, which have shown the best performance during the integration period. A 100 s water current is assumed from 1200 s to 1300 s.The IMM-aided ZUPT solution is evaluated during DVL outage. The observation vector of the former INS/DVL model is set zero to represent the pure INS model. The longitudinal velocity observation element of former INS/ZUPT model is set zero as no DVL velocity is provided, while the observation element of the lateral velocity remains the difference between the lateral velocity calculated by INS and the zero velocity. The performance of the proposed algorithm during the DVL outage is shown in In this work, an IMM-aided ZUPT algorithm is proposed for INS/DVL integrated navigation system to take the advantages of both ZUPT method and INS/DVL traditional method under the complex underwater environment. Due to the unstable temperature, water density and salinity, the residual errors in the DVL velocity data will influence navigation accuracy of the underwater vehicles. To reduce the negative effect of the DVL velocity error on the whole system, the ZUPT solution is introduced to mitigate the lateral velocity and position errors. However, as a water current often exists, the performance of the ZUPT method would be largely influenced. Thus, IMM algorithm is introduced to form a novel IMM-aided ZUPT methodology to deal with the complex underwater navigation situations.In the proposed algorithm, both INS/DVL model and INS/ZUPT model are constructed and operated in parallel. Through the IMM algorithm, the weights of the two model are calculated according to their innovations and innovation covariance matrices. Thus, the system can employ the two models properly and simultaneously. Meanwhile, the proposed IMM-aided ZUPT solution can also be used when DVL information is unavailable. By setting the corresponding elements of the observation vector to zero, the INS/DVL model turns into the pure INS model, while the ZUPT model for the integration turns into the ZUPT model for the standalone INS. The proposed methodology can continuously improve the navigation accuracy whenever DVL is available.Simulations are conducted to evaluate the proposed algorithm under both the integration period and standalone INS period. It can be seen that the proposed algorithm can leverage the strength of both the INS/DVL model and INS/ZUPT model. It is also effective during DVL outage. The proper model can be clearly seen by the model probability, where the non-ZUPT period can be clearly identified for both the integration period and standalone INS period. To conclude, in the complex underwater environment, where the water current often occurs, the proposed algorithm innovatively employs the ZUPT solution using the IMM algorithm, which is designed and is suitable for the land vehicle and ineffective when lateral velocity exists. From the simulation results, it can be seen that the solution will relieve the difficulties of the underwater navigation to some extent. In the future, the field tests need to be further carried out to validate this technique in various trajectories in the real underwater environment.


Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively. •The VCBA is a mathematical model that simulates in vitro fate of chemicals and the corresponding cellular effect.•The VCBA has been implemented in an open access web-based KNIME platform for ease of use.•KNIME Analytics Platform can be used to provide a user-friendly graphical interface for biokinetic models. These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The implementation and integration of such models within an automated and user-friendly computational platform will make them more easily accessible and applicable to support the chemical risk assessment process.http://www.cosmostox.eu/), with the aim to making the models publicly available in a user-friendly format. The models included prediction models for specific effects based on structure-activity relationships, absorption models to support extrapolation from oral to dermal exposure, especially relevant for cosmetics substances, as well as the biokinetics models described here.The development of computational tools and predictive models to support the safety assessment of chemicals, in particular cosmetics-related substances, was the goal of the EU COSMOS project (The VCBA can be used to support (i) the design of in vitro High Throughput Screening (HTS) experiments; (ii) hazard identification (based on acute systemic toxicity); and (iii) QIVIVE approaches to help risk assessment decision making. The VCBA is a mathematical model that was built using the R language and was applied to study the toxicological effects of chemicals on cells . The modmax and AUC), these models can support the submission of drugs to be evaluated by medical agencies; they can be used to support chemical risk assessments; as well as informing experimental design. In the last decade there has been an increase in development of PBK models, because of more accurate simulations of in vivo adsorption, distribution, metabolism and excretion processes compared to the classical PK models  will give rise to a model that can be applied to extrapolate from an in vitro concentration to the external dose of exposure. In vitro to in vivo extrapolation (IVIVE) is a useful approach in the prioritization of chemical testing in chemical risk assessment . To betthttp://www.knime.org/) is a free, user-friendly graphical workbench for data analytics including data management, data transformation, investigation, visualization and reporting. The COSMOS biokinetic models have been implemented in two KNIME versions, so that they can be executed either locally on a desktop computer  or directly online from a web browser using a KNIME WebPortal . With a view to promoting end-user acceptance of these KNIME-based workflows, bespoke documentation is available . As described above, these models consist of many parameters that are feeding the equations, physicochemical, cell line and experimental characteristics. The use of such models entails a need to manage a large and diverse set of data, from parameters to in vitro dose response curves analysis. The best way is to implement good data management practice and develop automated workflows. The KNIME Analytics Platform (2http://www.knime.org/) and R is a language and environment for statistical computing and graphics (http://www.r-project.org/). KNIME consists of a series of pieces of program code called nodes that can be connected in such way that the input of one node is the output of the previous one. Each node has a dialog in which the user can configure the operation of the node.We implemented the PBK models, VCBA, and the IVIVE approaches as open source platforms using KNIME (version 3.0) and R programs (which both are freely available). The KNIME Analytics Platform is a user-friendly graphical workbench for data analysis  or in the KNIME Analytics Platform as locally installed version in a desktop computer.This VCBA KNIME implementation includes three important features:In the following, we will introduce and explain the most significant nodes that build up the KNIME workflow as represented in Single Exposure mode of operation describes the process for a single exposure of the chemical to the cells. The present version can be run for several chemical concentrations. From one run to the next all parameters are reset to initial values except the initial chemical concentration. The other mode is Repeated Exposure, which describes the effect of several cell exposures of a single chemical concentration. From one run to the next, the last values of chemical concentration inside the cell and the number of cells are considered as the initial values for the next exposure, whereas the initial chemical concentration remains the same. The toxicological effect of cell exposure to chemicals in the VCBA is described by two parameters: the No Effect Concentration (NEC) and kt . The values of NEC and kt are then optimised by comparing the simulated (Viabilitysim) and experimental (Viabilityexp) cell viabilities and by minimizing the following error function:The automated VCBA KNIME workflow can be run in several modes. The optimization check gives us a graphical interface which is used to validate the values of NEC and kt. Paired values of chemical concentration and viability are introduced in a List Box Input node, while the initial values of NEC and kt are in two Double Input nodes. The Optimization Check functionality was separated from the Parameter Optimization to enable available values of NEC and kt to be entered directly without the need to run the time-consuming Parameter Optimization Mode every time.The The building blocks of the three zones are explained in more detail in the following section.Input Zone is used to feed the model with the necessary input data. It consists of a series of nodes where files are uploaded to input and select data for chemicals and cell lines:•Data related to the cell line such as duration, mortality, mass, volume in each cell stage, cell duration see .•Data related to the organic compound. By uploading a file containing the information on the parameters for the organic compound to be run. The chemical data needed to run simulation are chemical name (Chem name), CAS number (cas), logKow, molar volume, atomic diffusion, MW, water degradation, air degradation, and Henry's law constant see .•Toxicological data: the NEC and kt for each chemical and cell line see .•Experimental set-up data: The Tissue Culture (TC) plates node is a table that contains information on the technical specifications of the plates in the High Throughput Screening (HTS) as well as non-HTS experiments. These data are used to compute properties related to fate and transport, such as chemical binding to plastic or evaporation across the air-water interface see .•Data to be simulated .1) The •List Box Input node separated by “,”.Single Exposure: Several chemical concentrations to be simulated are introduced in the •Double Input node, the interval between doses and the number of repeated exposures in an Integer Input node.Repeated Exposure: Unique chemical conc. in a •List Box Input node pairs of chemical concentration and viability are introduced and in two Double Input nodes, the initial values of NEC and kt. In the case of single and repeated exposure the values of NEC and kt are in the toxicological data table.Parameter Optimization: Is used to optimize values related with toxicological data: NEC and kt. In a For data to be simulated, the data introduced depends of the mode of operation:Integer Input node), Initial number of cells (Integer Input node), Assay Volume (Double Input node).To run the model, the following additional information is needed: Total time . To perform this, we create a function called coreModel inside the R snippet node. This function is an R function that has five input parameters: chemical concentration in the medium, chemical concentration inside the cell, the number of cells, NEC and kt. The coreModel function is called several times depending on the operation mode. In the case of single exposure, coreModel is called one time for a single chemical concentration to be simulated whereas the rest of parameters remain constant. In the case of repeated exposure, there is a single chemical concentration exposure and the chemical concentration inside the cell and the resulting number of cells is taken into consideration for the next exposure. In the Parameter Optimization mode, the core model calculates the simulated viability for each chemical concentration based on the initial values of NEC and kt given in the two Double Input nodes. The results of the R Snippet node are output using the knime out variable.2) The l script  and execkage see . The VCBOutput Zone: The output from the R Snippet node is sent to several output nodes to provide and visualize the results. For tabulated data results, KNIME offers a variety of nodes that can store data. For graphical results, R Table View node is a KNIME node that generates graphics using R code. To show the results in a KNIME Server a series of additional nodes are needed. For graphics, each R Table View node is associated with an image output node. For small tables, a Textarea Output node can be used inserting the flow variable values into a structured html table. Furthermore, two report nodes provide a downloadable report of the results.3) 2.2max values . To describe different exposure we used the same workflow with two different R codes describing oral and dermal exposures, respectively .The PBK model KNIME workflow is set up in a similar way as the VCBA one, with an Input, Model and Output zone. The input parameters, physiological data and physicochemical parameters are stored in a file and upload in the node. The range of time and doses  must be provided. The R code representing the PBK model is stored in the model zone of the workflow and the output is reported as graphical representation of concentration time profile curves per target organ and as a table to be able to extract AUC and C2.3The KNIME workflow built for extrapolating from an in vitro concentration to an in vivo exposure doses (IVIVE KNIME workflow) was built in three parts, input, model, output zone . The input parameters are the same as reported for the PBK model  with addition of the cell model (thus cell type properties) and toxicity effect , so to be able to simulate dynamic effect (i.e. viability). The present workflow included three R codes, the first one representing the PBK model, the second one built to allow extrapolation of the number of DNA adducts formed and the third one to allow the extrapolation from a range of concentrations simulated to a range of exposure doses and to link to the effect based on the output of the first R code (PBK model). The extrapolation was done using the dose response curve obtained by the PBK model simulation. So far these workflows are chemical specific, and only 3 workflows were built for estragole, caffeine , and cou2.4http://knimewebportal.cosmostox.eu) and is supported by COSMOS Space (http://cosmosspace.cosmostox.eu), a facility to share predictive toxicology resources. The COSMOS Space provides free registration to login to the COSMOS KNIME WebPortal  and hosts the workflow documentation as well as user guidance. Web tutorials for the different workflows are available from the COSMOS project website .The COSMOS KNIME WebPortal is freely accessible  Double Input node and so on, as described previously for the input zone.The first screen in the COSMOS KNIME WebPortal is a textual introduction to the model . Clickin33.1Now that the VCBA automated tool is built and ready to use  we will show some concrete simulation results using two chemicals, acetaminophen and caffeine, as illustrative examples. The first version of the model (2015) works in single dose mode for the following compounds: acetaminophen, verapamil hydrochloride, acetylsalicylic acid, maprotiline, cycloheximide, sodium lauryl sulphate, tert-butylhydroperoxide, valproic acid, rifampin, thioridazine hydrochloride, caffeine, colchicine, acrylamide, diphenhydramine, pentachlorophenol, disopyramide, chloroquine diphosphate, tetracycline hydrochloride, amiodarone hydrochloride, carbamazepine, HPTE, pyraclostrobin, diquat dibromide, abamectin, bisphenol A, and benomyl. The VCBA currently provides repeated exposure simulations for caffeine, amiodarone, acetaminophen  of no effect (safe) exposure levels .The characteristics of the VCBA KNIME workflow and how it can be applied to provide a user-friendly graphical interface for analysis of several outcomes was illustrated, to predict time concentration profiles, such as intracellular concentration from the VCBA. The first generation of the KNIME VCBA workflow included a drop down list reporting 35 chemicals , for whiThe VCBA and PBK models fit into a suite of predictive computational models, which have been developed in the EU COSMOS project to support the safety assessment of chemicals, and in particular cosmetics-related substances. These tools are based solely on in vitro and in silico predictions thus promoting the 3Rs approaches. The biokinetic workflows are complemented by a range of chemistry-based workflows developed within COSMOS to support the safety assessment process, with a focus on cosmetics-related substances, for which the dermal route of exposure plays a major role . Within http://knimewebportal.cosmostox.eu/), without the need to install any software locally. The WebPortal allows access to the KNIME Server and execution of workflows through a web interface from any recent web browser, without knowledge of the KNIME software as such. COSMOS Space (http://cosmosspace.cosmostox.eu/) hosts the workflow documentation and user guidance, including a list of all available workflows. Web tutorials for the workflows are available at http://www.cosmostox.eu/what/webtutorials/. The description of the PBK models and the VCBA can also be found in the EURL ECVAM database, the DataBase service on Alternative Methods to animal experimentation (DB-ALM) (Method Summary no. 162). DB-ALM is a public database service that provides evaluated information on development and applications of advanced and alternative methods to animal experimentation in biomedical sciences and (regulatory) toxicology .In the interests of transparency, extensibility and ease of use, the developed COSMOS models have been implemented in KNIME and made publicly available as open-source, automated tools . KNIME iIn conclusion, the COSMOS biokinetic models in the COMSOS KNIME WebPortal allow for an intuitive step-by-step execution, but are restricted to certain pre-configured settings. The use of the COSMOS biokinetic model workflows in the KNIME Analytics Platform, on the other hand, gives end users more freedom in executing and refining the model parameters and input data according to their own needs. At the present time, these automated workflows cannot be used to replace the need for expert judgement within the risk assessment process. However, we anticipate that their use will not only expedite the safety assessment process, but will also ensure reproducibility and traceability in some of the key steps.


Mobile technology has become a ubiquitous technology and can be particularly useful in the delivery of health interventions. This technology can allow us to deliver interventions to scale, cover broad geographic areas, and deliver technologies in highly tailored ways based on the preferences or characteristics of users. The broad use of mobile technologies supports the need for usability assessments of these tools. Although there have been a number of usability assessment instruments developed, none have been validated for use with mobile technologies.The goal of this work was to validate the Health Information Technology Usability Evaluation Scale , a customizable usability assessment instrument in a sample of community-dwelling adults who were testing the use of a new mobile health  technology.A sample of 92 community-dwelling adults living with HIV used a new mobile app for symptom self-management and completed the Health-ITUES to assess the usability of the app. They also completed the Post-Study System Usability Questionnaire (PSSUQ), a widely used and well-validated usability assessment tool. Correlations between these scales and each of the subscales were assessed.The subscales of the Health-ITUES showed high internal consistency reliability . Each of the Health-ITUES subscales and the overall scale was moderately to strongly correlated with the PSSUQ scales (r=.46-.70), demonstrating the criterion validity of the Health-ITUES.The Health-ITUES has demonstrated reliability and validity for use in assessing the usability of mHealth technologies in community-dwelling adults living with a chronic illness. Mobile technology has become a nearly ubiquitous technology in the United States, where almost two-thirds of the population owns a mobile phone, but also worldwide with more than half of the world population owning a mobile phone . Mobile Concurrent with the proliferation of mobile technology has been the vast increase in the number of mobile health  apps available to consumers. For example, in 2013, a report by the IMS Institute for Healthcare Informatics estimated more than 40,000 health-related iPhone apps were available for consumer use . By 2015The dramatic growth of mHealth technology is not surprising given that mobile technologies are a novel method for the delivery of cost-effective, timely, and relevant health promotion and management information . Mobile Use of mobile technology as a platform for behavioral interventions adjuncts to health care delivery and monitoring of health status has widely proliferated in the past 5 years. Despite this growth, few mHealth apps or interventions have undergone systematic and rigorous usability evaluation prior to their dissemination ,12. UsabAnother barrier to usability assessments is that a dearth of validated instruments for assessing mHealth technology persists -16. ImplIn our earlier work, we presented preliminary validation of the Health Information Technology Usability Evaluation Model , a theoretical framework to guide usability evaluations for assessing mHealth technologies . The modA need for a validated usability instrument for mHealth technologies persists. In response to this need, we sought to validate the use of the Health Information Technology Usability Evaluation Scale  in a sample of users of a mHealth app developed for community-dwelling adults living with HIV. The Health-ITUES, derived from the Health-ITUEM, is a validated instrument that explicitly considers task by addressing various levels of expectation of support for the task by the health information technology (IT). The Health-ITUES also has the added benefit of being customizable, which can address the study needs and concepts measured without item addition, deletion, or modification. This is an important benefit of this instrument because it allows for harmonization of findings across studies. The factorial validity and internal consistency of the Health-ITUES was demonstrated through an exploratory factor analysis . The devThese data were collected as part of a larger study for developing and testing a mobile app (mVIP) for symptom self-management in persons living with HIV. The goal of the parent study was to translate a paper-based manual that had been effective in ameliorating symptoms in persons living with HIV onto a mobile platform to promote dissemination of patient-centered outcomes research. As part of the parent study, we developed a beta version of the app and conducted end-user usability testing with 20 participants in a laboratory setting. Following refinement of the app based on our findings during our usability assessment, we tested the mobile app in a 12-week feasibility trial with 80 end users. Participants were randomized to an intervention or control group, but both groups received a version of the mobile app. The intervention group received an enhanced app, which included self-care strategies for 13 symptoms commonly experienced by persons living with HIV.Study participants were recruited from June 2016 to February 2017 through in-person and email recruitment. Recruitment sites included HIV clinics and community-based organizations. Eligibility criteria included: (1) diagnosed with HIV, (2) older than 18 years of age, (3) able to communicate in English, (4) experienced at least two of 13 HIV-related symptoms in the past week, (5) met the cognitive state minimum score  measured by the Mini-Mental State Examination (MMSE) , and (6)Participants in both groups were given access to the app on a mobile phone. Following log-in and use of the mobile app, participants completed a number of surveys, which are detailed subsequently. The surveys collected demographic information, the Health-ITUES , and theThe Health-ITUES is a customizable questionnaire with a four-factor structure. The Health-ITUES explicitly considers a task by addressing various levels of expectation of support for the task by the health IT system, and it has been validated through exploratory factor analysis and confirmatory factor analysis in a sample of nurses who used a Web-based nurse-scheduling tool. The Health-ITUES consists of 20 items rated on a five-point Likert scale from strongly disagree (1) to strongly agree (5). A higher scale value indicates higher perceived usability of the technology. Items in each of these scales and how they were customized for this study are illustrated in A self-reported sociodemographic questionnaire was developed to collect information on the participants’ age, gender, race, ethnicity, sexual orientation, education, annual income, relationship status, and a previous acquired immune deficiency syndrome (AIDS) diagnosis. Participants were also asked about their technology use with questions specifically asking about most frequently used type of mobile device, frequency of use of this device, and whether they use apps on their mobile phone.The PSSUQ is an instrument for assessing user satisfaction with system usability and was developed as a usability assessment tool that was specifically for use in the context of scenario-based usability testing, although additional research has indicated that this may be useful for field evaluation as well ,30. FactDescriptive statistics were used to calculate demographic characteristics of the study sample. We used the Kruskal-Wallis test to determine if there was a relationship between any of the demographic data and each of the Health-ITUES constructs: impact, perceived usefulness, perceived ease of use, and user control.Psychometric test theory involves the development and evaluation of clusters of questions called scales, which are used to gather information about the quality of psychological measures . The HeaVariability refers to the extent to which the full range of scale scores and item responses are reported in the data. Optimal variability is denoted by a full range of responses of the scale. Scales that are skewed, either negatively or positively, tend to be less responsive to the effect of usability errors. To ensure limits in the variability, the frequency of missing data needs to be limited and randomly distributed across participant responses. To address skewness, we used a nonparametric method  to test the known-group validity. For the other analyses, such as Cronbach alpha and Pearson correlation, normality is not an assumption, so no log transformation was performed in this analysis.Internal consistency reliability is a measure of the how well the instrument measures different constructs and delivers reliable scores. Internal consistency reliability measures whether several items that propose to measure the same general construct produce similar scores, indicating the homogeneity of a scale or subscale. The Cronbach alpha coefficient provides an estimated score of the internal consistency reliability based on all possible correlations between items collected at any time point . CronbacValidity refers to how well the scale measures the attribute it claims to measure ,36. TherCriterion validity is the extent to which a measure is correlated with a validated outcome measure . To asseA total of 92 persons completed the Health-ITUES survey. Twenty participants completed the survey as part of the end-user usability testing of the beta version of the app and 72 persons completed the survey on enrollment into the 12-week trial. As noted in The range, mean, median, and standard deviation for each of the subscales’ scores at baseline are reported in Variability was evaluated for each of the subscales. The full range of responses was not observed for any of the Health-ITUES subscales. Slightly more than a half (59%) scored the maximum score for the quality of life scale, a third (36%) scored the maximum score for the perceived usefulness scale, slightly more than a half (60%) scored the maximum score for the perceived ease of use scale, and more than a third (42%) scored the maximum score for the user control scale. No users scored the lowest possible score. Overall, the users rated the usability of mVIP app as being high. The full range of usability scores was not demonstrated because no users rated the app as the lowest possible score.By examining the boxplots and the raw data, extremely low scores were treated as outliers and were removed from the reliability and validity analysis. There is evidence that all the subscales were negatively skewed, indicating more favorable usability scores.r=.82, r=.63, and r=.56, respectively), which is evidence of convergent validity and discriminant validity.Internal consistency reliability, as measured by the Cronbach alpha coefficient, is reported for each of the multi-item scales in t test, therefore we used a nonparametric method, the Kruskal-Wallis test, to detect differences between the intervention and control group scores. Using the Kruskal-Wallis test, we found a statistically significant difference between the intervention and control group on all subscales expect impact.Known-groups validity, another subtype of construct validity, was evaluated by measuring differences in mean scale scores at baseline between the control and intervention group . As noteP<.001 level. Correlations ranged from .46 to .70. None of the correlations were greater than .80 or were very strong and thus there was no redundancy in subscales [Criterion validity was measured through the assessment of the concurrent validity by comparing the measure in question, the Health-ITUES, with an outcome assessed at the same time, the PSSUQ. ubscales . The leaThis paper presents the validation of an instrument for measuring usability of mHealth technology. The validation study reported in this paper was conducted to explore the psychometric properties of the Health-ITUES, a customizable usability evaluation instrument that includes subscales of impact, perceived usefulness, ease of use, and user control in a sample of community-dwelling adults living with HIV who were users of a mobile app for symptom self-management. The Health-ITUES varies from most traditional measurement scales in that it is designed to support customization at the item level to match the specific task/expectation and health IT system while retaining comparability at the construct level. The results of this study support the construct and criterion validity and reliability of the Health-ITUES for usability assessments of mobile technologies.r=.46-.70). Information quality, a subscale on the PSSUQ, had the lowest correlation , which is to be expected because the constructs are substantively different. The overall scale scores for the PSSUQ and the Health-ITUES had the highest correlation . This would be expected because the two usability scales have some overlap in their constructs. At the same time, the overall correlations were only moderately strong [In this study, the reliabilities of the four Health-ITUES subscales and overall score were examined in terms of internal consistency. The internal consistency reliability of each of the subscales were high but less than the Cronbach alpha=.95 threshold suggesting that there is no indication of redundancy in scale items. Moreover, a comparison of the results from the Health-ITUES to the PSSUQ had moderate to strong correlations (y strong , suggestA strength of this validation is our study sample, which further supports the utility of this instrument for usability assessments for consumer health informatics. Our study sample is comprised of nearly all racial and ethnic minority persons and those persons of the lowest socioeconomic groups in the United States. Nonetheless, our study participants were owners and frequent users of mobile phone devices, demonstrating the widespread use of mobile technology and the relevance of mHealth technology across end users. In contrast to the earlier development and validations of the Health-ITUES in nurses using a Web-based system ,26, thisConstruct validity was assessed by measuring differences in mean scale scores at baseline between control group and intervention group participants. Statistically significant differences between groups were found in the expected direction for all subscales except impact. These trends were expected because at baseline we would not expect to find a significant difference in impact as a result of the study intervention. In contrast, a statistically significant difference in perceived usefulness, ease of use, and user control was demonstrated, with intervention group participants finding the mobile app to be more usable than control group participants did.The construct validity of the measure was also supported by the degree to which interscale correlations corresponded to what was expected. The higher correlations among the impact and perceived usefulness subscales than between the other subscale items supports the convergent and discriminant validity of this instrument because participants who perceive the technology to be useful are more likely to show an improvement in their impact as a response.There are a number of limitations of this validation study. First, most of our study sample were mobile phone users and so we were unable to validate the use of this instrument as a usability assessment tool in non-mobile phone users. This limitation is mitigated as the number of mobile phone users continues to grow across geographic regions and socioeconomic groups. A second limitation is that these findings may not be generalizable beyond our study population. Further research is most definitely needed to further validate this instrument in other study populations. Given the dearth of current instruments for usability assessments, we believe that the findings from this study provide an important contribution to the literature and an opportunity for future study of this instrument with additional study populations.This study provides preliminary evidence to support the validity and reliability of the Health-ITUES for usability assessments of mobile technology. In light of our findings, the authors recommend use of the Health-ITUES as a measurement tool for assessing the usability of mobile technologies. Validation of the Health-ITUES is an important step in ensuring that usability is attended to and maintained in mHealth technology interventions. This work is particularly important given the proliferation of mobile technologies and the push for consumers to take control of their own health and become greater users of technology for the acquisition and delivery of health information. Further, these findings will hopefully stimulate additional research and practice to ensure the usability of mobile technologies and particularly for those tools that are developed for consumers’ use.