Every year, about 1.2 million people die through road traffic crashes worldwide. Majority of these deaths occur in Africa where most of their emergency medical services are underdeveloped. In 2004, Ghana established the National Ambulance Council to provide timely and efficient pre-hospital emergency medical care to the sick and injured. Pre-hospital emergency medical service is essential for accident victims since it has the potential of saving lives. The study sought to determine the relationship between pre-hospital trauma survival rate and response time to emergencies and factors associated to pre-hospital trauma survival  in Accra, Ghana.The study was a cross sectional study which reviewed pre-hospital care forms of trauma patients from the fourteen ambulance stations in the Greater Accra region from January to December 2014. Data were extracted from these forms and the response time estimated. Conscious patients who were alert were categorized as responsive under the AVPU scale. The proportion of patients who survived pre-hospital trauma and  the time pre-hospital trauma cases were responded to was estimated. Multiple logistic regression analysis was conducted to determine which variables were associated with survival.A total of 652 pre-hospital care forms were reviewed. About 87% survived pre-hospital trauma. The average response time to patients was (16.9 ± 0.7) minutes and the median transportation time of the patient was 82 min. Level of consciousness of a patient and response time of patients transported was found to be significantly associated with pre-hospital trauma survival.There was a high trauma patient survival rate among victims attended to by an NAS. The average response time in Greater Accra region in the 14 ambulance stations is 16.9 min which is not different from the 17 min recorded in 2013 by NAS. Factors that were associated with pre-hospital survival were alertness in the level of consciousness and response time less than 17 min. Each year about 1.2 million people die through road traffic crashes in the world . The GreTrauma refers to bodily injury resulting from the application of an external physical force . It rangPre-hospital Emergency Medical Service (EMS) is the first level of treatment given to patient suffering life threatening conditions by emergency medical technicians (EMTs) and paramedics before they are handed over to the hospital. EMS seeks to reduce response time. That is the time between an emergency call made to an ambulance station and the time a dispatched ambulance gets to the scene for assistance. A shorter response time is known to greatly improve survival .The occurrence of the May 9th Accra sports stadium disaster in 2001 which led to the loss of about 127 lives, provided the leverage for the establishment of the national ambulance service (NAS) in Ghana. The core mandate of the service is to provide timely and efficient emergency medical care to the sick and injured and to transport them to an appropriate facility. The target set for response time in Ghana is eight 8) minutes  were reviewed and data extracted.The Greater Accra Region occupies a total land surface of 1.4% of the total land area of Ghana. Its capital is Accra, which is also the capital of the Republic of Ghana. This region is the second most populated region in the country after the Ashanti Region. Greater Accra Region has a population of 4,010,054, accounting for 15.4% of Ghana’s total population.The NAS has fourteen ambulance stations , Amasaman, Ashaiman, Atomic, Dodowa, La General, LEKMA, Accra Sports Stadium, Ridge, Tema, and Weija) which offers service to 62.5% of the region and record an average of 1358 cases annually. The region was chosen because it has 14 ambulance stations and heavy traffic making it difficult for movement of ambulances to the scene of emergencies.We reviewed records of all trauma cases in the Greater Accra region transported to various health care facilities by the 14 ambulance stations in 2014. These records were obtained from a PCR forms registry provided by NAS. The study focused on trauma which was defined in the context of road traffic crashes. Trauma was limited to road traffic crashes patients who have been conveyed to various hospitals by the national ambulance service.Records of patients who sustained pre-hospital trauma and were directly attended to by the National Ambulance Service as well those who were picked by private or commercial vehicles to clinics and subsequently transported to an appropriate hospital by NAS in the Greater Accra Region were included in the study.Data from patients with ineligible or missing records that were difficult to decipher were excluded and all pre-hospital trauma (stable) cases that were transported for diagnostic purposes such as X-ray and CT scan in the region were as well excluded. All the cases that the time of transfer from site of injury to the hospital could not be ascertained were not included in the study.This data was extracted from the records of operations department of the NAS where Pre-hospital care forms filled by EMTs for each patient they transported are kept. Research assistants were trained to help in the extraction of the data. Stations were also visited to confirm and clarify inconsistent data. This was done by going through their occurrence books at the stations.Patient characteristics: age, sex, type of injuryTime the emergency call was made to the stationTime the ambulance departed to scene,Time the ambulance arrived at scene time,Time the ambulance departed the scene to the health facilityTime the ambulance arrived at the health facilityThe time the patient was handed over to the health facility.Data extracted from the forms were:Data extracted was entered into Microsoft Excel 2010 to estimate the following variables: Response time, transportation time and time spent on scene.Response time was estimated as the duration between the time a call was made for the emergency and time of arrival of the ambulance at the scene of the emergency.Transportation time was estimated as the duration between when the call was made to the station and the time the patient was handed over to the facility.On-scene time was estimated as the duration of time the ambulance spent at the scene of the emergency before transporting the patient to the health facility. That is time of arrival at scene of emergency to time of departure.Case handling time was estimated as time the patient is taken from the scene to the time the patient is handed over to the facility.EMTs on the field sort patients according to their degree of injuries and transport those with life threating conditions but salvageable injuries first as it is done in triaging. However, this process is not color coded or recorded on their PCR forms.Conscious patients that were alert were categorized as responsive under the AVPU scale.Analysis was done using the Stata Version 13. Response time in minutes was categorized as < 4 vs. ≥4, < 8 vs. ≥8 and < 17 vs. ≥17 min respectively. The proportion of patients who survived pre-hospital trauma as well as the proportion of pre-hospital trauma cases that was responded to within eight minutes was estimated. In line with Eisenerg and colleagues findings of response time between 4 and 8 min increasing the rate of survival for cardiac patients, we determined the four (4) to eight (8) minutes response times .p-value of less than 0.05. A multiple logistic regression was conducted to test the association between pre-hospital trauma survival and the significant dependent variables while adjusting for age, and sex.Bivariate analysis was conducted to determine which variables were associated with survival. Chi-squared test of proportions was used to determine the relationship between independent variables , transportation time (< 60 vs. ≥ 60 min), level of consciousness, type of injuries and age) and dependent variable . Level of significant association was determined at 95% confidence interval and The study protocol was reviewed and approved by the Ghana Health Service Ethical Review Committee of the Research and Development Division of the Ghana Health Services GHS-ERC: 59/12/15. In addition, permission was obtained from the National Ambulance Service to use their data for the conduct of the study. Patient identifiers such as name and address were not extracted. To assure confidentiality, the forms were kept in locked cabinets and the data entered kept in password protected files on the researcher’s personal computer.The 14 ambulance stations of the National Ambulance Service from January to December 2014 transported a total of 20,236 patients. Of these 652 were trauma cases that had all been involved in road traffic crashes.About 70% (435/652) of the trauma cases transported were male. The mean age was (33.0 ± 18.6) years. Almost 66% (415/652) of them were within the age group 15 to 44 years.The mean response time was (16.9 ± 0.7) minutes, with a range of (1–151) minutes. The median time spent on the scene in managing patients was 17 min with a range of (1–150) minutes. The median transportation time for patients was 82 min with a range of (5–552) minutes.A total of 8 patients did not survive pre-hospital trauma, only one 12.5%) had a response time of < 8 min. However, no significant difference was found between the patients’ characteristics and response times of eight minutes. (Table 2.5% had p < 0.05). Other patient characteristics observed such as transportation time and type of injury were not found to be associated with survival. The other response time categories were not significantly associated with survival . This is similar to a study conducted in Germany in 2012 which also found no significant association between survival advantage for trauma patients with shorter pre-hospital transportation times [The median transportation time estimated was 82 min with a range 5 to 552 min. This means that after an incident occurs, it takes an ambulance approximately 82 min to get the case to the hospital. In trauma management, it is believed that the first one-hour after trauma incident is crucial to the patient getting to the emergency department in the hospital in order to improve patient outcome. This principle is usually termed as the golden hour guideline. The NAS is therefore not able to meet the golden hour guideline in trauma cases. Thus morbidity and mortality are likely to increase significantly since the injured person does not receive definitive care within 60 min of injury . In our on times . Anotheron times . Shorterp > 0.05).The median time spent on scene managing trauma patients was 17 min with minimum of 1 to maximum of 180 min. The platinum ten-minute guideline recommends that patients should be managed for a maximum of 10 min in trauma situations. Spending more than ten-minutes on the scene can lead to splinting patient to death. It is necessary to initiate transport and continue with treatment on the way to the hospital. The first 10 min is crucial in the management of patients. When the platinum 10 min is violated, it decreases the chances of survival . HoweverOur results further suggest that, the level of consciousness is significantly associated with pre-hospital trauma survival. The odds of surviving a pre-hospital trauma increased when a patient was conscious and alert. This confrims the findings of  a study in Germany that concluded that patients with Glasgow Coma score of 3 (unresponsive patients) had a poor outcome and even with treatment only 5% of them had good recovery .p > 0.05). We rather observed 17-min response time to be associated with trauma survival (p < 0.05). Since Ghana’s current response time is 17 min, reducing the 17-min response time has the potential of improving pre-hospital survival. Blanchard and colleagues found that after controlling for several important confounders, such as severity of injury, age and sex, the response time standard of within 8 min was not associated with improved survival which supports our findings [Response time of 8-min standard was not also significantly associated with trauma survival in our study (findings .Ideally, response time would have been appropriately estimated as the time the accident occurred to the time the ambulance arrived at the scene. However, it was not possible to determine the exact response time, since the EMT was not present at the time of the accidents. We therefore estimated response time as the time between the call being made for the ambulance to the time the ambulance arrived at the scene. Also, distances corresponding to various transport times were not recorded on the PCR forms, which made it virtually impossible for us to factor in an important possible predictor like distance in the study. However, the location of the emergencies was indicated on the form and gave a fair idea of distance.There is a high trauma patient’s survival rate among victims of accident attended to by the Ghana National Ambulance Service. The average response time in Greater Accra region in the 14 ambulance stations is 16.9 min which is not different from the 17 min recorded in 2013 by NAS. Factors that were associated with pre-hospital survival were alertness in the level of consciousness and response time less than 17 min. NAS should put in measures to reduce the response time and also put in structures to measure fractal response time. Public education on how to reach an ambulance, what should be done when there is an emergency are some of the efforts that have been made to reduce the response time.


These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feedback from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions personalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation.Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to identify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primitives. These primitives are transformed and evaluated into higher level abstractions such as  Computer games have become an integral part of modern leisure-time. There is intense competition among game companies as they are being faced with challenges to retain user engagement in a saturated market. Steels , based oSerious games, and in particular simulators, offer a medium for training and evaluating individuals in high risk scenarios, including for example flying, driving or performing surgery. Since individuals differ in terms of skills and preferences, a variety of training methods should be adapted to maximise training outcomes. In tasks where the end goal is similar between individuals , people with less experience will need more assistance to reach the desired level. As we will clarify later, we relate this amount of assistance to the user’s Zone of Proximal Development (ZPD) Vygotsky , and we In this paper, we focus on learning a user (or player) model using a combination of behavioural and physiological data. The model infers the current user’s experience, attention and performance from combinations of extracted features while playing a car racing game. We monitor these abstractions, update the user model and provide decision adjustments for the alteration of the racing track according to the user. We propose an algorithm that monitors the user performance and modifies the game experience in real-time, with the purpose of maintaining high player satisfaction and enhancing the learning process.flow  state, avoid sensory overload, and remain highly engaged  is doing well in the game or tries to see if the physiological data are coherent to the user’s game model. To determine the attention metric, we first evaluate the average proximity (experience) of the user to the expert model using only non-physiological data. Getting high values from user input and game output metrics shows that user is performing well with respect to segment times and racing lines; therefore, attention should be high. This is based on the assumption that in order for the user to accomplish high non-physiological (game related) values, the user should be highly engaged in the game. Otherwise attention is evaluated from the current variations (exploration) of the user’s physiological data. Since the data are relative to those of the expert’s, positive physiological exploration means that the current features of the user are closer to the expert’s.The main assumption underlying the implementation of these three concepts is that we are considering the expert model, which the user is compared with, as optimal in respect to engagement and performance levels. It is what the user should imitate and achieve, whereas any deviations from that are conveyed as lack of skill, challenge or attention. It is also important to mention that the model uses both physiological features from external sensors and behavioural data from the game and calculates the significance of each feature obtained according to the time performance of the user in a path. Merging the data from both domains gives the model more potential to explain the events that are unfolding in the game. Multimodal player models have been reported to be more accurate and match the user’s responses better than corresponding models built on unimodal data  or if the user is engaged with the task through self-motivation to succeed .Both Experience, High Exploration: Challenges are much greater than the skills of the user. The user is performing poorly even when different techniques are being tried. If this state continues to persist then user’s anxiety level will increase; therefore, in order to push the user back in the engagement-training region we should drop the difficulty level of the game.Low Based on the calculated values of the notions and the hypotheses, the model outputs decisions on whether a path should become easier, challenging or kept the same.These three high level concepts monitor the user experience while playing the game and can describe the state and engagement of the user with the game according to the combination of their values. Based on the theory of Player modelling in games has received a lot of interest in recent years. The primary goal of player modelling is to understand the interaction of the player experience at an individual level. This can be either cognitive, affective or/and behavioural. There have been many different approaches for the understanding of a player in games .Player modelling is further split into three approaches:Rating-based format using scalar/vector values [e.g. The Game Experience Questionnaire .Preference-based format by contrasting the user experience between consecutive gaming sessions.In addition to the user annotation methods reviewed by Yannakakis et al. , that give rise to game Dynamics (how mechanics behave on user inputs) and fuses to game Aesthetics (user experiences invoked by the game). Hunicke et al. , people with lower experience would have a larger ZPD and will need more assistance to reach the desired level. As shown in Sect. Zone Theory to explain human development in educational setting, describing the process that establishes the development of a skill in an individual. Valsiner (The Zone of Free Movement (ZFM) represents a cognitive structure of the relationship between a person and the environment in terms of constraints that limit the freedom of these actions and thoughts.The Zone of Promoted Action (ZPA)—defines the set of activities, objects or areas within the environment from which the tutor is promoting to the tutee. With the tutee having no obligation to accept it.The Zone of Proximal Development (ZPD) from Vygotsky —defines These zones constitute an interdependent system between the constraints enforced on the environment of the learner and the actions being promoted for the learner where both constraints and actions are imposed by external stimulus.Valsiner ’s develoValsiner ’s theoreThe development is optimised when the relationship between ZPA and ZPD is such that what is promoted (ZPA) lies within the individual’s ability to achieve (ZPD). The ZPD cannot be fully incorporated in the ZFM. Since ZFM is determined by the tutor and a tutee can only develop aspects of the ZPD that have been advertised (ZPA) and not restricted (ZFM). When all zones merge it emerges “a family of possible novel forms of change” where ZPD is dependent on the state of the ZFM/ZPA complex Galligan .self-scaffolding  can operate. This is the car simulator, the hardware utilised and the various tracks that are available. The ZPA represents the constraints that are promoted by the framework. The model tries to reach the engagement and learning goals set on development, and through the feedback that it receives from the user’s actions, it generates new tailored tracks that advertise new spaces (tracks) where learning and development are most likely to be realised for a particular user. An optimised relationship between ZPA and user’s ZPD is realised through the new track, however, the user has to willingly co-operate, with their engagement and attention, for the learning and development to take place, as suggested by Blanton et al.  through Valsiner .In this section we describe the algorithms adopted to perform feature extraction from the user’s data.Low Level Primitives  algorithm was chosen. AP  shown in Table Our approach to the user model implementation utilises a sequential hybrid modelling approach and integrates several algorithms, techniques and theories from literature. A top down view of the user model shown in Fig. Low Level Primitives) are being converted into Performance Metrics by comparing them against the user’s previous “best” data or the expert’s data. Each metric has been given a certain weight through a linear model (Weighting Model), forecasting against the time taken for a certain path to be completed. Through the utilisation of Trace Theory, the performance primitives—along with their weights—are transformed via game specific rules, into variables of the concept of flow . This concept can evaluate the challenges that the user is facing (Exploration), the skills that the user attained (Experience) and the Attention that the user is paying to the task. From then on, the algorithm tries to balance those abstract concepts according to the individual by giving certain instructions to alter the track.Real time raw data from the user input, game, eye tracker and head pose , extensively listed in Table Segments can either be a turn, a straight line or a chicane. The advantage of this method is that the user is being monitored more frequently than once every lap and also our metrics reflect on the performance of more primitive paths where it is easier to identify the specific weaknesses of the driver. As seen in the bottom part of Fig. The raw data of Table User inputs These are the principal links between the game and the user. The user has three main inputs to the car simulator: the steering wheel, the throttle and the brake pedals. For each of the three series of data we compute the mean value for a particular path to create the corresponding primitives.We also perform the Affinity Propagation (AP) clustering method, described in Sect. Game outputs The simulator we are using provides real time output data of the car and the environment’s states with a high sampling rate (100 Hz). By using the lap number, time and path position we are able to create indices at the points at which each segment starts and ends for each user, so we can split and group the data together. As game output primitives, we extract the number of collisions per second for each segment. We also store primitives of the user’s path as well as the user’s path accompanied with the car speed.Eye tracker From the eye tracker we obtain four kinds of information; a parameterised real world position of the eyes, the eye gaze on the screen, the eye gaze fixations and the presence of a user. Through a combination of this information, we calculate and define as the user’s primitives  of the virtual world in the game. The similarity measure used for both clustering approaches is the Euclidean distance of the 2 and 3 dimensions respectively.RGB-D and head pose Apart from using the cameras to record the experiments, we also use the depth information to find the head pose of the user. Incorporating the algorithm, described in Sect. performance metrics so that we can assess the efforts of the user towards the task.After the primitives are computed, they are stored into the lowest level of the user model. The next step is to convert them into An interesting study by Hong and Liu  analysedlow level primitives involves their conversion to performance metrics. From each category in Table In the user model structure see Fig. , the layA collection of data for a segment is defined to be more optimal than others according to the amount of time the segment took to complete. As a result, we would expect that the “expert driver” would have the fastest possible time on that segment. In our experiments, the expert driver for each segment was defined at the end of the data collection process by selecting the user with the fastest segment times.time performance (No. 9) is being divided by the optimal one for each segment. Cluster centres  are being matched with their closest optimal centre and the average value of their Euclidean distance is obtained.For each of the primitives in Table For Nos. 7 and 8 we use a different approach, since they consist of a sequence of data. In general, to find the dissimilarity of the path covered between two different sequences of car positions, it would be wrong to compare the points one-to-one since there is a high probability that the sequences are not of the same size . A solution is to use a form of dynamic time warping between the two sequences so that the temporal domain is abstracted out and we are only comparing the spatial domain which is the path. In our case, we simply find the two closest points of the optimal path to each of the user’s points in the path. We then calculate the point that is perpendicular to the vector joining the two optimal points and the user’s point.expert driver and in red is the recorded user’s trajectory. If we zoom in on the points, as shown in Fig. A and B, which are two consecutive points of the optimal path and C. C is a point created by the path of the user in the track. Our algorithm tries to find the closest points from the optimal path, A and B, for every user point, C. It then finds a point D on the vector C to its closest point on the optimal path. However, the path consists of discrete points and therefore, a perpendicular point on the path might not exist. D is found by optimising the solution for the 3 dot product equations shown in  they are comparable against each other, without the need of rescaling, and also (2) outliers are brought closer together. Regarding the latter, when two users perform poorly in a metric, their values are expected to be well beyond and below the mean of an average user. However, the two values could still be far from each other. This doesn’t provide any more information rather than a poor performance from both on the particular metric.The use of exponential functions helps to decrease that gap and keep the linearity of the rest, up to the point where the original value is significant. For example, if two users’ paths have a huge difference from the expert’s then we know that both users’ time and path performance would be low. However, the time versus path performance proportionality is not as linear as the ones coming from average-high metric performances.X, of the expert to expert comparison. The Constant, C, for each different metric is determined by solving , and the %Metric variable to 50%.The exponential function used to convert the metrics to percentages is shown in . Each pe solving . This isIn racing, “time” is the most general and important metric that can reflect on the performance, skills and engagement of the user to the task. However, we have to get deeper into the data in order to understand whether the driver actually achieved a good time. In our user model we try to justify and break down the time taken by the user, through a particular segment, using several descriptive metrics. Therefore for finding the best “weight” of each metric towards the user’s segment time performance, we used various modelling approaches; whereas the best weights describing the correlation  from ourAll includes all metrics in Table Non-Physio consists of all metrics obtained from the user inputs and game outputs and Physio includes metrics associated with the user’s physiological data, such as those obtained from eye tracker and head pose. Time performance is not included in any groups since it is used as reference variable in  The main issue with both of the previous approaches is that the Spearman correlation calculates the significance of each metric without considering the correlations between them as well. If two metrics are highly correlated to each other then one of them can be removed since this makes the whole model simpler and more robust to small changes. In order to reduce those redundancies and find the best weights that describe  The linear model was forced to provide either positive or zero valued coefficients C was unconstrained) for solving ((LM Pos Raw) The linear model was bound to calculate positive coefficients but metrics were not converted into percentages. The coefficients found for each segment (LM Raw) In order to check that our reasoning for positive bounds was valid, we used the same approach as 2 but with no positive bounds. Therefore, coefficients could be negative as well.(LM * OPT) All of the above linear models are set with the maximum number of metrics available. However, this might impact on fitness of the model since they could provide coefficients that are overfitting the data due to the high complexity of the model and the degrees of freedom introduced. Therefore, using all of the three methods mentioned , we found the optimal combination and number of metrics of each method that gave the best linear model by finding the lowest calculated root mean square error (RMSE)—all combinations were tested using exhaustive search. and the summation of the left side of  didn’t substantially improve their correlation result but through the plots we can spot that the optimal models corrected some peaks in the values. Each dot in the figures represents a segment trial from a particular user. Therefore, each dot is a set of metrics collected from a respective segment (multiplied by their respective weights), plotted against the time metric that is normalised so we can compare all the data from all segments together.The most correlated model, among all groups of metrics, was the Framework is the LM Percentage (LM Perc) since it gives the highest Spearman correlation and also the steeper linear curve in all the results. The weights used for each metric generated from expert comparisons are shown in Tables The model selected for our The metric-weight pairs described in the previous sections specify the significance of each metric to the good time performance of a particular path. They are the fundamental information that transformation rules are using to define the higher level concepts.Transformed Modelled Traces in Trace Theory): Experience, Exploration and Attention The value of each class is determined by a combination of the performance metrics analysed according to certain game-specific rules:Following the theoretical frameworks of behavioural analysis like the Concept of Flow Steels , the ZonExperience The skills of the user, is determined by the proximity of the user’s primitives to the expert’s. Since the metrics are now expressed as percentages, the higher the value the better the user is performing on that particular metric. Although the significance of this metric to the user’s skill is determined by the weight calculated using the chosen linear regression method (LM Perc). Skill cannot be determined by a single group of values. It has to be an overall value of several trials. Therefore, it is calculated by the weighted sum of the mean of each performance metric over a certain number of laps, as shown by  is the range of actions that the user is using in the current task. Through the metrics we define this as the weighted sum, of a metric’s absolute shift to a proportion of its mean (defined as “jump”). If the difference between two consecutive metrics passes a fixed percentage value of the current experience then the value is positive, otherwise it is negative. This is expressed by  whereas attention defines the range they are operating in, as shown by Fig. attention is low then the flow region is much narrower, therefore, experience and exploration values should be treated with more care than when attention is high. Our approach is that thresholds define low/high experience/exploration values in high attention, whereas in low attention they are compared with each other.The high level variables mentioned in Sect. Attention, defined through a threshold, and:Experience and Exploration are High: The user is in the optimal region so the segment should be kept the same.Both Experience and Exploration are Low: The user is engaged since Attention is high, therefore we should allow more time for the user to adapt and build up skill so the segment should still stay the same.Both Experience, Low Exploration: A more challenging track should be provided (e.g. one with more or sharper curves) since the user’s skills are high and challenges drop below the threshold.High Experience, High Exploration: An easier segment should be created  since the user’s skills cannot cope with the current challenge of the path.Low When the algorithm detects low Attention, Experience and Exploration values are compared to themselves and not to a threshold, therefore:5.Experience larger than Exploration: Skills are greater than the challenges so the path should become more challenging.6.Experience lower than Exploration: Skills are lower than the challenges of that path so an easier path should be provided.7.Experience same as Exploration (both low): Both values are low and Attention of the user is also low therefore an easier segment should be provided.Using the above information and Fig. Giving instructions to change the track at this point is straightforward and is shown in Fig. jump—J) that can be altered as user spends more time with the game. In this article we are not focusing on how these parameters are changing the outcome of the algorithm, however, the reported values  were designed to give values equally in the negative and positive region.Specifically, the two variables representing the “jump”  responses we created a much elaborated description of our users .Some users commented on the fact that there should have been more categories to rate themselves, especially towards the higher range. Also, we notice that only 37% of the users changed their skills rating by a level, as shown by Fig. see Fig. b. Howevesee Fig. c. PeopleSelf-Rate and Re-evaluate skill groups  gave very low p values  were tested with each other using Kruskal–Wallis test.The next step is to find particular patterns in the users’ responses and further understand their profile type. The game specific questions labelled as: p value . Responses from each self-rate group towards game play frequency response are shown in Fig. Low Beginners. Therefore, assuming all users start at scoring their improvement from the same point then more experienced users will improve more than the rest.An interesting correlation was also detected between Self-Rate and Improved responses with track difficulty  for the What follows in the rest of this section is the validation of the algorithm’s high level variables and outcomes through the user responses:Algorithm’s outcome variability and user’s self-rate response Chi-squared test was applied to assess the goodness of fit for the variability of the algorithm’s output on the users’ level of expertise. Users were split into three groups according to their combined self-rating response mentioned in Sect. easier, same or challenging. These were set as the category names of the Chi-square table.null hypothesis states that the decisions for each group are being drawn from the same distribution and that categories are independent from the groups. The test rejects the null hypothesis with a Chi-square statistic at By counting the number of category outputs generated from all segments for each of the three groups, we create the Chi-square matrix shown in Table easy: 1, same: 2, challenging: 3) we calculated the average score for each user from the generated outputs. Utilising Kruskal–Wallis test with the null hypothesis that the scores of each group arise from the same distribution gave p value null hypothesis. Box plots of the average score against the groups are shown in Fig. Furthermore, by assigning a score value for each category . These responses were paired with the algorithm’s average Attention value of each user. Using Kruskal–Wallis test  of our user model  and 29.5% differ mostly towards higher levels: same and challenging  test between paired decisions of the two models, showed that there was a statistically significant change of outcomes between expert and user’s “best” models . In future work we will tackle this issue by personalising the thresholds (see Table user’s best model case) so that the two models are coherent.This increment is expected mainly for two reasons. Individual user models might:As executed in Sect. Kruskal–Wallis test between the average score obtained for each user from the generated ordinal output decisions and the user’s combined self-reported skill gave advanced users are well separated from intermediates and beginners. However, intermediates and beginners share some overlap. The Spearman’s correlation supports a positive correlation of expert’s model segment decision score versus skill comparison  User satisfaction and feedback through questionnaire; (2) by generating a user specific outcome associated with user responses. By applying several statistical tools, we associate the user responses to specific algorithm variables and outcomes to confirm that the algorithm is both in-line with users perspective and with the assumptions we made to design the model. By rejecting the null hypothesis in each of our tests in Sect. Experiencedistinguish between the skill level of each user by evaluating the Explorationkeep track of the improvement of the user through Attention through the dataspecify the user’s create personalised outcomes for each particular user which are still conform to their skill groupexpert datafunction and elicit similar results without using be further configured through model’s parameters to fit user progressCompared with similar model approaches specified in Sect. Our objective is to enhance the gaming experience by modelling the user using behavioural and unobtrusive physiological data, and customising the designed tracks in a racing game. By utilising theoretical frameworks on learning and development such as the concepts of onnaire;  by generBacklund et al.  found pobeginner vs advanced) their generated tracks developed either easier paths  or harder ones. We showed that the sum of the generation of our ordinal output instructions is correlated to the ordinal level of skills of the user. Therefore, our adaptive user model framework behaves in a similar manner to their findings. Moreover, our model also integrates attention and exploration concepts in order to describe the current state of the user and adapt better to the individual.In addition, Togelius et al.  created Framework. This can be achieved by modelling the users through a multiple track scenario consisting of personalised (experiment) and fixed (control) transitions of tracks that would allow us to observe the engagement and training through the groups of users.Our future direction is to further validate our hypotheses and the specific variables of the non-physiological data of the Low Level Primitives level, such as the game outputs and the user inputs are specific to the task. These can be assigned as anything from mouse clicks to explicit complex actions that are significant to the progress of the competing task.The Weighting Model level, the designer should specify the target or a combination of them that provide a measure of global performance to the task . Moreover, the regression model can be switched with any other similar machine learning technique that provides weights for every input towards a specific output.For the Theoretical Framework chosen for the task. In our case we are interested in the development of the user’s driving skills and the engagement of the user to the game. From our chosen psychological frameworks, these are evaluated through Experience, Exploration and Attention. Thus there is a need for three transformation rules in the Trace Theory level, which are specified by the task designer/expert.The last three higher levels are interrelated to the Goal level utilises the thresholds set on the evaluated high level variables and the outcome of the model depends on the task .The Example: Training children with cognitive difficulties to use a wheelchair Our model could be used to identify particular weaknesses a child possesses when handling a wheelchair—via the model’s weighted metrics—when the Task is to drive through a narrow door or performing a turn in a corridor. The Goal could be to either alter the scenario to fit the particular user  or personalise the assistance that gives to the user.The proposed method of the adaptive user model is based on having independent hierarchical levels. Thus each level can be modified to fit the requirements of the user activity that needs to be modelled:Low Level Primitives as in our case, could consist of user’s driving inputs, eye gaze/fixations, blinks, head pose, wheelchair’s path, velocities and angle, task completion time, collisions etc. Whereas the Weighting Model target could be a combination of the path-angle metric, since this is the most important aspect in this task. As this task also involves learning, development as well as engagement of the user and the task involves driving, the Theoretical Framework and transformation rules (Trace Theory level) could be the ones we already proposed.The The main limitation of the studies we reported in this paper is that the evaluations are based on self-reported user data using Likert-scale self-reports. It is difficult to assess whether responses are reliable or if they are affected by other factors that haven’t been considered. An argument emerges that it is not sufficient to assume that users are capable of understanding their own performance and potential. Therefore, responses tend to be subjective to the particular person and the situation s(he) provides them, and thus can be biased.Framework through the direct comparison between the user’s performance and engagement in different trials.However, we addressed this issue by (a) collecting data from many users (52) so that the results are statistically significant and (b) carrying out user profiling tests to first check that our user models are varied and to identify any patterns or irregularities between their responses  we determined, which will be addressed in future work. Furthermore, the evaluation of the Attention variable is based exclusively on a game-determined approach and does not take into account any latent emotional states of the user. In addition, the selection of our sensors influence our model’s operation; we chose sensors that are unobtrusive to the user and the task. However, this can sometimes cause data loss or inaccuracy since they have a finite area of tracking. The user model can be designed to adapt to these problems by continuously re-calculating the weights, although an extended loss or stream of erroneous data can lead to an inadequate user model. In our future work we are planning to employ additional physiological sensors  that will enhance the evaluation of user’s immersion to the task.On a more technical detail, we employed linear based models to calculate the weights used to evaluate the high level variables in the flow and Zone of Proximal Development). Our intention is to provide a flexible algorithm that identifies the on-line weaknesses and performance of a race driver so that we can alter the track’s path to fit the individuals level of skill. The functionality of the framework so far is to provide segment alteration instructions  and also point out the human factors, that lead to the particular decisions. The design and implementation of a new path as well as the experiments regarding that, are outside the scope of this paper.In this article we propose an adaptive user modelling framework for transforming user’s low level primitives composed of game-related user actions, game outputs and unobtrusive physiological data such as head pose and eye tracking into theoretical frameworks of learning and development (e.g. concept of Low Level Primitives) and estimate the engagement of the users to the task, through their comparisons to an expert—a trained and self-engaged—user. In addition, simple and logical rules regarding the racing task were embraced in order to perform the Trace Theory’s transformation process of low level traces to the theoretical frameworks. The fitness of the rules and in general the modelling process was verified by associating user responses to the experiment with the offline instructions generated. User profiling helped us understand the variation in the user types, whereas feedback helped us to improve our future experiments. An interesting result is that our user responses were not found to be statistically different between genders as indicated in Backlund et al. (Real user experiments using a simulator setup allowed us to calculate the correlations of our extracted features (d et al. .Framework without the need of user responses. As far as we are aware, this kind of experiment hasn’t been conducted before. However, the forthcoming main challenge is to elicit a new path that fits the user model. The path construction algorithm has to include both the instruction generated and the specific human factors that lead to the decision in order to create new segments that will both improve the engagement and assist the training of the user.The future development of the user model is to verify the results through experiments where the track is being altered through the decisions of the model in real time, which will allow the user to drive the new track straight away. User attention, skill and challenges will be monitored with the aim of keeping above a threshold through the whole process. This approach will also validate the An important future aim will be the adaptation of that model structure to another game or task through minimal changes. This will prove the transferability of the model with as few modifications possible and show its generic capabilities to capture the engagement and enhance user training.


Modelling wind speeds in urban areas have many applications e.g. in relation to assessment of wind energy, modelling air pollution, and building design and engineering. Models for extrapolating the urban wind speed exist, but little attention has been paid to the influence of the upwind terrain and the foundations for the extrapolation schemes. To analyse the influence of the upwind terrain and the foundations for the extrapolation of the urban wind speed, measurements from six urban and non-urban stations were explored, and a model for the urban wind speed with and without upwind influence was developed and validated. The agreement between the wind directions at the stations is found to be good, and the influence of atmospheric stability, horizontal temperature gradients, land-sea breeze, temperature, global radiation and Monin-Obukhov Length is found to be small, although future work should explore if this is valid for other urban areas. Moreover, the model is found to perform reasonably well, but the upwind influence is overestimated. Areas of model improvement are thus identified. The upwind terrain thus influences the modelling of the urban wind speed to a large extent, and the fundamental assumptions for the extrapolation scheme are fulfilled for this specific case. The initial research interest focused on large-scale effects3 and was subsequently spurred by the interest in the problems of air pollution5. The topic of urban wind speed for air pollution applications has remained an active area of research until today11. In recent years, the urban wind speed has as well been of interest for urban wind energy (e.g.14) or assessments of wind loadings on tall buildings15.The urban wind speed has been an area of research, as part of urban climatology studies, since the 1930s17 have shown the importance of this input. These models are characterised by being fast, generally applicable, reliable, and simple to use20. The same principles continue to guide the development of such models even today despite the evolution of high performance computing facilities. This is the case, since the needs have evolved and increased as well, demanding longer and finer time scales and more and diverse receptor locations. In line with this approach, focus of the present study is on modelling hourly averaged wind speeds in the roughness sublayer (using the terminology of21), since this is the averaging time and relevant height used in regulatory air quality models.Often, financial and technical constraints prevent the use of measurements to assess the urban wind speed. Models of the urban wind speed are therefore often used to estimate the urban wind speed at many locations. The present study is motivated by the use of the modelled urban wind speed as input to regulatory air pollution models, since previous work23).Large-scale numerical weather prediction models (e.g.24).Physical models (e.g. wind or water tunnels) (e.g.26).Computational fluid dynamics (CFD) models  is using an extrapolation approach across multiple internal boundary layers following either32 and33 or34. Apart from the studies in Table 435, fitted straight lines to scatter plots of measured rural and urban wind speed data from Birmingham, UK (2 × 4 weeks); Copenhagen, Denmark (1 year); Lisbon, Portugal (3 months); and Barcelona, Spain (1 year). Their study was part of European Cooperation in Science and Technology (COST) 7156 and concluded that the relationship was site specific and required more investigation. The many different approaches presented in Table 36, and given the vast number of approaches a comparison is beyond the scope of the present study.A number of recent models of the urban wind speed using analytical horizontal and vertical extrapolations have been presented in the literature37. This development in measurements has, to the best of the authors’ knowledge, not been reflected in the modelling. The influence of the upwind terrain above the blending height has been modelled13, and the upwind influence as a function of four wind directions  has been modelled as well28. As seen from Table Previous work on the influence of the upstream terrain on urban wind speed measurements was reviewed, and a variation of a factor of two in the drag coefficient for approximately 20° wind direction change was shownadvanced model) and without (henceforth referred to as the simple model) upwind influence, was developed and validated using the measurements. Given the challenge of this task, with respect to both modelling and measurements, the present model should be considered a first step in the direction of a more generally applicable urban wind speed model.To asses the validity of the assumptions for the extrapolation technique, exploratory data analyses of meteorological data were performed. To assess the influence of the upwind terrain in models of the urban wind speed, a new model for the urban wind speed, with  calculated based on meteorological data from Meteorological Model 5  on a 5.6 km × 5.6 km resolution for the year 2012. This indicates that wind speed and wind direction are the determining variables among the inputs available.In an attempt to explain the remaining variance in the measurements, scatter plots of the remaining variance were produced and these showed only vague connections to the other meteorological variables measured The analysis of the agreement in wind direction between the stations and the influence of atmospheric stability can be found in the Supplementary Material.40, and mixed results have been reported as to the influence of atmospheric stability in urban areas43. In these cases, a more advanced modelling approach might be required.The above analyses show that the present case study is dominated by mesoscale weather patterns, which allows the use of the presented extrapolation scheme. Other cities have reported temperature gradients up to 8 °Cmodel is used to mean the advanced model, since the simple model will have the same spectra as the measurements. Since HCOE is the longest time series in the present study, the periodogram of the covariance between the wind speed at this station and the wind speed in Kastrup is shown in Fig. To analyse the properties of the different ways to represent wind speed data for two stations , the spectral representation was analysed. In this section, the term y-axes in Fig. The periodogram of the relative wind speed is shown in Fig. Figure The model output and the measurements as a function of wind direction can be seen in Fig. For Kastrup→HCOE the advanced model reproduces the wind direction dependency with good performance. HCOE is, as seen in the maps in the attached Google maps file, influenced by a large park for easterly winds and subsequently influenced by a large high rise building at 140° to 150°; these features are nicely reproduced by the advanced model and not seen in the simple model. This pattern is, to some extent, also seen for HCOE→Jagtvej  and HCOE→HCAB  and in Kastrup→Jagtvej due to the short distance between HCOE and Jagtvej. Both the advanced model and the simple model are underestimating the wind speed for westerly wind directions, more so for the simple model than the advanced model for this station. This can potentially be connected to some areas of detached houses upstream of the Kastrup station whose influence is underestimated in both models. This is emphasised by the fact that all models underestimate westerly wind speeds when Kastrup is used as input.HCAB is influenced by an amusement park with correspondingly low roughness for south-westerly wind directions thus giving a characteristic wind direction pattern. This pattern is overestimated in the advanced model for HCOE→HCAB and not present in the simple model. The underestimation for Kastrup→Jagtvej for south-westerly winds can potentially be linked to something (probably the aforementioned detached houses upwind of the Kastrup station) not accurately modelled in Kastrup. This is the case since the wind direction pattern is nicely reproduced by the model when HCOE is used as input. HCOE→Jagtvej is nicely reproduced by both models. This means that this represents a particularly simple situation where a simpler approach can be useful. The reason is the short distance between the stations meaning that the wind speed and direction will be extremely identical for the two stations.Tirstrup→Aarhus  is a special case since the airport wind speed is only slightly larger than the urban wind speed. For a small interval around 200° the urban wind speed is actually larger than the airport wind speed. This is the case since the urban station is only a few meters higher than the airport station. Moreover, the airport station is influenced by forest areas close to the station. Nevertheless, the model underestimation for the advanced model is more pronounced for this station compared with the other stations. For the interval The comparison between the simple model and the advanced model indicates that the upwind influence is overestimated in the advanced model for some wind direction intervals. This can be seen in e.g. Kastrup→HCAB A similar pattern can be seen in Table To analyse the dependency on wind speed, the relative deviation entclass1pt{minimahr. For HCOE→Jagtvej the performance of the two models is approximately equal whereas for HCOE→HCAB the simple model performs better. This can be explained by the large low-roughness areas (large parks/amusement parks) close to these two stations influencing u*. Future work should aim at reducing this effect in the advanced model.A similar trend can be seen for the slope and intercept of the trend line for the extrapolations in Table The histograms of the residuals between the input and the measurements and the results from the models and the measurements can be seen in Fig. As can be seen, the longer the extrapolation distance the wider the distributions. This is again caused by turbulence in the atmosphere on all length scales between the two stations. Reducing the spread further will be challenging given that this turbulence is not measured in the present project.16 for an atmospheric dispersion model.The upwind terrain influences the modelling of the urban wind speed to a large extent. Modelling this effect is a significant challenge which in the present paper has been met to some extent. The model results show (except Aarhus) good agreement between the models and the measurements compared to model results given in e.g.The influence of the upwind terrain was shown to be overestimated in the model, and future work should aim at reducing this effect. Considering that the model contains many uncertain parameters, this result is acceptable.The agreement in wind direction between the stations were shown to be high. Moreover, the influence of atmospheric stability, horizontal temperature gradients, land-sea breeze, temperature, global radiation, and Monin-Obukhov Length were shown to be small. One remaining question is whether this finding is only valid for the present location or it represents a more general phenomenon.16, could be performed. This could subsequently form the basis for studies on the comparison of the available parametrizations. Likewise, comparison studies between different types of models  would be relevant to highligt strengths and weaknesses of different modelling approaches.Many alternative parametrizations to the ones selected in the present study exist. To direct future research to the parametrizations with the largest influence on the model, sensitivity analyses, in line with the ones presented inIn the present study, the urban areas are assumed to be flat although the height above sea level varies by several tens of meters. The influence of the urban topography is likewise a potential area for a future study.Modelling the wind speed in the urban canopy layer is complicated and several parametrizations has been proposed in the literature. One remaining question is the validity of these parametrizations, where measurements in the canopy layer, wind tunnel studies, and the use of CFD  models could contribute to answer this question.−1 have been removed (with the exception of the classification into atmospheric stability classes). This was done to avoid the difficulties of the anemometer start up speeds, and since these data will be disproportionately influenced by turbulence. Moreover, the given uncertainty of 0.1 m s−1 on the cup anemometers amounts to 5% at 2 m s−1 . This means that the measurement uncertainties will be high for these data and they are thus excluded. Data above 10 m s−1 are likewise excluded to reduce calculation time. This is not expected to influence the results, since these data constitute a very small fraction of the total data.In the analyses of the present study, wind speeds below 2 m s38. The details of the meteorological instruments used in the present study can be found in Table mast type measurements in Table −1). The data scanning frequency of both the wind speed and the wind direction measurements is 0.1 Hz. These data is subsequently averaged to hourly wind speeds and directions. The data is subsequently quality controlled using the procedure described in16.Long term wind speed measurements have been performed in Copenhagen, Denmark at the H. C. Oersted Institute, Copenhagen University (HCOE) and in Aarhus at the town hall under the Danish Air Quality Monitoring Programme for the period 1994 to 2010 for HCOE and for the period 2001 to 2010 for Aarhusroof type measurements in Table 46). The sonic anemometer at Jagtvej was a RM Young 81000 with a specified accuracy of ±1% or ±0.05 m s−1 below 30 m s−1 and ±3% above 30 m s−1, and the sonic anemometer at HCAB was a Metek USA-1 with a specified accuracy of 0.1 m s−1 or 2% at 5 m s−1. Both sonics were sampling at 10 Hz. The measurements were despiked by removing measurements with accelerations larger than 5σ (where σ is the standard deviation of the hourly average) or velocities larger than 10σ. Following the recommendation from47, no rotations were applied to the sonics. This is because, the flow in the urban roughness sublayer is highly three-dimensional. The assumptions for the rotation techniques are therefore not fulfilled. The measurements were subsequently averaged to hourly values with the wind speed being the sum of the u, v, and w components.For the present study, a wind measurement campaign was designed. The campaign consisted of setting up two sonic anemometers on rooftops in Copenhagen, Denmark at respectively Jagtvej and H. C. Andersens Boulevard (HCAB) to measure the wind speed close to the roof level. The sonic anemometers were mounted 4 m to 5 m above roof level. This is referred to as the ncdc.noaa.gov). This means that the measurements were not performed by the authors but represent standard meteorological data publicly available. The wind speeds were obtained as METeorological Aerodrome Report (METAR) measurements. These have a wind direction resolution of 10° and a wind speed resolution of 0.45 m s−1 (1 mile/h). These were subsequently averaged to hourly wind speeds and directions. The details of the measurements are summed up in Table The wind speed measurements from Kastrup Airport and Tirstrup Airport were obtained from National Climatic Data Center, National Oceanic and Atmospheric Administration , also called the “effective geostrophic wind”, is calculated from a measured wind speed through an upwards vertical extrapolation. It should be emphasised that the macrowind is not necessarily equal to the synoptic geostrophic wind speed derived from macroscale pressure gradients31.The “macrowind” , at the location of the station, are calculated using the approach of48.The heights of the individual IBLs  is calculated using the approach of49.The boundary layer height .Sh is assumed to be constant between the two locations thus providing horizontal extrapolation.The process is repeated for the downwards extrapolation.The results are subsequently averaged over a 30° wind direction interval to account for hourly wind direction meandering.The model can be summarised in the following steps:hubl is a function of the wind speed. The above described wind speed extrapolation model was implemented in Matlab by the authors. Differences between the present modelling approach and that of previous studies are discussed in the Supplementary Material.Details of the calculations in the individual step can be found in the Supplementary Material. For the downwards extrapolation, Items 5 to 7 are iterated since 31 but applies several simplifying assumptions:z0 and d are calculated from the building height of the building where the anemometer is located (hr) rather than using the geographical preprocessor. The equations for z0 and d are simplified expressions of50 as follows:50. This recommendation has been implemented in the model. The airport stations are assigned a constant roughness length of 0.005 m (short grass) following51. The vertical extrapolation is performed with one roughness length and displacement height instead of using the above described IBL approach. This means that upwind influence on the station is not taken into account.It is recommended that the maximum allowed building height for the parametrisation is 20 mhr following the recommendation by52 thus also removing the wind speed dependency of the model.The horizontal extrapolation height is assumed to be 4To analyse the influence of the upwind terrain, a simpler model was developed based on the same principles as described above. The simple model also uses the extrapolation scheme fromsimple model in the results section.This approach is referred to as the Google Maps of the StationsSupplementary Material


Hemophilia is a congenital bleeding disorder with an estimated frequency of 1:10,000 births. Repeated joint bleeding is a hallmark of the disorder and leads to painful hemophilic arthropathy. Regular exercise can help improve joint stability and function, reduce the risk of injury and bleeding and improve physical fitness and quality of life. This method paper describes an online training concept aiming to offer access to appropriate exercise instructions for people with hemophilia who are not able to attend regular training at a hemophilia center.The online exercise program is accessible through the homepage of the Department of Physical Medicine, Rehabilitation and Occupational Medicine of the Medical University Vienna as well as through scanning a QR code printed on information material using a smart phone or tablet.The program contains exercises to improve mobility, coordination, muscular strength and flexibility. A brief introduction is given by a hematologist, a pediatrician and a physiatrist. An introductory video informs about contraindications and essential precautions, such as medical attendance and sufficient factor therapy to consider before starting the training. Another video gives advice on the exercise composition. The demonstrated exercises are explained by a physician and are available for adults and children. To individualize training recommendations and offer further diagnostic tools and physical treatment options as necessary, the Department of Physical Medicine, Rehabilitation and Occupational Medicine of the Medical University of Vienna will establish consultation hours for people with hemophilia.As hemophilia is an orphan disease, patients are mainly treated in specialized centers. For patients who live far from these centers or have limited access to a training there for other reasons, the physical medicine consultation hour and the implementation of online exercise instructions offer individually adapted exercise information for a regular home-based training to benefit from increased physical fitness and joint stability. Hemophilia is an X‑chromosome linked bleeding disorder caused by a deficiency of coagulation factor VIII (in hemophilia A) or factor IX (in hemophilia B) with an estimated frequency of 1:10,000 births . Current.Hemorrhages mainly affect joints  and muscles . Blood aSeveral studies have found deficits in the basic motor functions in people with hemophilia (PwH) –22. DefiIn a healthy population, improved fitness in endurance as well as muscular strength is associated with significantly better cardiometabolic risk factor profiles, reduced general mortality, a lower risk of developing functional limitations and possibly less symptoms of depression and fatigue . Resista.Exercise programs for hemophilia patients consist of several components that may be most beneficial when applied together: flexibility and stretching (including a warm up before activity starts to reduce the risk of injury), strength, sensorimotor training (or proprioception), balance as well as overall function , 44. FleAs hemophilia is a complex and rare disorder (orphan disease), PwH are mainly treated in hemophilia treatment centers that bring together healthcare professionals experienced in treating PwH . To imprThe chosen exercises should be safe when applied correctlyThe exercises should be practicable for most of the PwH including patients who already suffer from arthropathic joint conditionsThe training should be effective and should consider the most important deficits in PwHAs the videos are used by lay persons, instructions on how to choose, compose and perform the exercises should be simple and comprehensiblePatients should not need to buy expensive training equipment, this is why resistance exercises using body weight and therabands were chosenFor adults who have already developed chronically affected joints there should be the possibility to specifically focus the training on these jointsAs training is only effective if it is adapted over time according to the increasing fitness, the possibility to increase the training has to be part of the program.Implementing the online training program had the following requirements:The concept of the online training program was compiled in accordance with musculoskeletal experts in the field of hemophilia from Germany. The videos were recorded separately for adults and children. The video clips were shot with a person with hemophilia and the background of studying sports science (who demonstrated the exercises for the adult videos) and a 10-year-old volunteer (who demonstrated the exercises for the children videos). During the takes a physiotherapist was present to observe the correctness of the exercise performance.via links listed on the homepage of the Department of Physical Medicine, Rehabilitation and Occupational Medicine of the Medical University of Viennathrough scanning the QR codes that are printed on flyers which inform about the online video program orthrough opening the link printed under the QR code on the mentioned flyers.There are three options to access the webcasts:In opening videos specialists in the field of hematology, pediatrics and physical medicine and rehabilitation speak about hemophilia and exercise from their perspectiveAn introductory video informs about important preconditions such as sufficient factor protection and approval for the training and supervision through specialists . Patients are also informed about contraindications for the training and receive instructions in the event of bleeding.A video regarding the composition of the exercises explains how to choose the appropriate exercises and how to increase the training with improving fitness. Here, general recommendations are given. These recommendations may be individually adapted according to the joint condition and the individual fitness and needs by specialists who treat the patient.The exercise videos contain exercises for warm up and mobilization, coordination, strengthening and flexibility. As each webcast can be accessed separately, each contains a short disclaimer (repeating the most important precautions and pointing to the introduction and exercise composition webcasts). The disclaimer is followed by instructions regarding the specific motor skill and by the exercise itself. In those webcasts that offer variations or advanced exercises, the respective videos are accessible one after the other as part of the same webcast by selecting the appropriate slide in the navigation bar. The exercises are demonstrated in the video, a physician comments on the exercises and points out what to pay attention to when performing the training.The webcasts are structured as follows:Table As the primary aim is to offer an exercise program to better stabilize the joints, training is focused on warm up and coordination, strength and flexibility exercises. Endurance training and the participation in sports are also recommended in PwH, but a detailed instruction on the choice of sports and the risk assessment of the different types of sport would have gone beyond the scope of the online training program. Regarding endurance training and sports, PwH are asked to follow the recommendations of their specialists in charge. Specific recommendations in this respect can be found in the pertinent literature , 47–57.Increase the number of repetitions: starting with only few repetitions (to learn the motion sequence appropriately), then increasing up to 20–25 repetitionsIncrease the load/weight (increase the load of the theraband)Increase the range of motionIncrease the number of sets performed (up to three sets)Finally, an exercise of higher difficulty can be chosen. When doing so, it is recommended to start again with a low number of repetitions, in slow motion and a small range of motion and increase as given above.The training should to be increased over time according to the improving fitness, which is why the presented coordination and strengthening exercises can be adapted in difficulty, intensity and volume. Coordination exercises can thereby be increased as soon as an exercise can be safely conducted. Strengthening exercises should be increased in consultation with the specialist in charge and in the following order:Coordination and strengthening exercises should not be increased both at the same time but one after the other. In the case of swelling, pain or symptoms of a bleeding, the training should be stopped and a physician should be consulted. The training composition differs between the children and adult training program and is explained in the webcast on how to compose and increase the training. Adults are advised to focus their training on the chronically affected joint. In children who have not yet developed arthropathic joints, the training is recommended to strengthen the main deficits in children, which are coordination, flexibility of the back side of the legs and the strengthening of the hip abductors and abdominals , 57, 58.Both adults and children are advised to warm up and mobilize the joints and the spine before starting the training. It is especially important for adults to warm up appropriately with a focus on the chronically affected joints and the structures that are to be trained afterwards. Adults should take a minimum of 10 minutes for warm-up and mobilization before starting the actual training. Thereby, structures are prepared for the training, the risk of injury is reduced, and the joint range of motion is maintained. Also, PwH are advised to make use of the joint range of motion that is possible but to stay in the painless range.Coordination exercises are important to improve balance and movement patterns and reduce the risk of injury. It is recommended to perform coordination exercises at the beginning of the training after warming up when the person is still in a resting state. In coordination as well as in strengthening exercises the main focus should rest on improving body awareness and increase the neural control of the movement according to the integrated model of joint function modified by Herbsleb et al. –63, wherStrengthening exercises aim to stabilize the joints and the spine. Sufficient warm up before the training is important. As already mentioned, a slow, focused and concentrated training, starting in a small range of motion, is recommended. To properly learn the movement, few repetitions are suggested to start until the exercise can be performed confidently. One repetition should last 5–7 seconds. PwH are advised to keep breathing steadily and not to hold the breath. If the exercise is already learned properly, adults can exhale during effort and inhale during relaxation.Flexibility exercises help to maintain or improve the joint range of motion and reduce the risk of muscle shortening/contractures. Adults should stretch after performing strengthening exercises at the end of the training, children should ideally stretch before and – if there is enough time – also after performing strengthening exercises. Stretching should be done equally on both sides with 3 repetitions per exercise in a pain-free range, each stretch lasting approximately 20–30 seconds. In general, all exercises should only be practised when they can be performed without pain. General recommendations regarding the composition of the 6 exercises are given in Table To start, the training composition is given in order to help PwH focus on the areas that may be the most effective in improving function and joint stability over the first 6–8 weeks; however, the recommendations can always be and should be individualized by the physician in charge. Over time, PwH are asked to vary the training by keeping the focus on one or two strengthening exercises as recommended to start with  and in addition add one or two strengthening exercises for other muscle groups and keep that scheme for another 6–8 weeks. Coordination and strengthening exercises should be increased as given above according to the increasing fitness.In order to take account of international recommendations to individualize the training for PwH according to the individual’s fitness, joint status and needs , 50, theIn German-speaking countries attempts have already been made to educate PwH in multimodal disease-specific exercises and offer instructions to continue with a home-based training throughout the year. Sport camps take place once or twice a year and last for several days which are organized by the Austrian Haemophilia Society and the Interdisciplinary Center for Motion and Sports Medicine Wuppertal in cooperation with the University of Wuppertal in Germany and Switzerland , 65. ForThe online videos presented above aim to additionally support PwH with a regular home-based training. They also offer basic information on how to start training after being evaluated through specialists regarding sufficient factor therapy and contraindications for participating in the program. Data regarding the acceptance of the online program will be collected and presented at a later date.Earlier studies concluded that exercise interventions for PwH are associated with specific challenges, such as the risk of injury, overload and potential bleeding. However, well-managed participation in exercise with adequate factor therapy and with respect to the individual’s joint status is known to improve a number of physical parameters , 50, 66.Regarding strength training, static, dynamic and isokinetic exercises can be a part of a rehabilitation program for PwH as also recommended by Pietri et al. . ExercisThe recommendations presented in the videos can always be adapted and increased individually by the physician in charge. According to the literature, strength training recommendations should be used as a guide only. The appropriate exercise should always be adapted to the individual’s needs and should be prescribed by healthcare professionals trained in hemophilia care who ensure the appropriateness, sufficient factor therapy and protective gear if necessary , 50. TheThe cited Cochrane review on the safety and effectiveness of exercise in PwH assessed many study interventions that trained several motor skills . RegardiAccording to the American College of Sports Medicine (ACSM) guidelines for resistance training for healthy adults, dynamic training with intensities of at least 40–50% of the one-repetition maximum (or the appropriate correlate in maximum isometric force) with ≤ 15 repetitions is required to improve muscle strength. The existing literature suggests that in children and adults with hemophilia not only training in intensities according to the ACSM resistance exercise criteria but also low resistance dynamic, isokinetic and partly also isometric combined with dynamic training of sufficient frequency and duration seem to be able to increase muscular strength , 73–79. http://www.meduniwien.ac.at/physmedrehab-videos.Increased physical fitness is known to have multiple benefits in PwH. Sufficient strength, flexibility and coordination are important to improve joint stability and function, increase bone mass and reduce the risk of injury which might possibly reduce the risk of bleeding. Therefore, a regular multimodal training is important for PwH. As the distance to hemophilia treatment centers with professionally guided exercise training may be a challenge for PwH who live too far away to participate regularly, the online video program was implemented to close the gap and offer easily accessible and free of charge training recommendations that can be applied at home on a regular base. It is important that the patients participating in the training are under sufficient factor therapy according to their hematologist or pediatrician. To ensure safety, the training should be prescribed and supervised through musculoskeletal specialists with experience in the field of hemophilia and should be adapted individually according to the individual’s needs. One possibility to do so is the consultation hour at the Department of Physical Medicine, Rehabilitation and Occupational Medicine at the Medical University of Vienna. Further information and access to the online videos is available through the clinic’s homepage: Data regarding the acceptance and utilization of the online video program will be collected and presented at a later date.