Extremely catastrophic situations are rare in Sweden, which makes training opportunities important to ensure competence among emergency personnel who should be actively involved during such situations. There is a requirement to conceptualize, design, and implement an interactive learning environment that allows the education, training and assessment of these catastrophic situations more often, and in different environments, conditions and places. Therefore, to address these challenges, a prototype system has been designed and developed, containing immersive, interactive 360-degrees videos that are available via a web browser. The content of these videos includes situations such as simulated learning scenes of a trauma team working at the hospital emergency department. Various forms of interactive mechanisms are integrated within the videos, to which learners should respond and act upon. The prototype was tested during the fall term of 2017 with 17 students (working in groups), from a specialist nursing program, and four experts. The video recordings of these study sessions were analyzed and the outcomes are presented in this paper. Different group interaction patterns with the proposed tool were identified. Furthermore, new requirements for refining the 360-degrees interactive video, and the technical challenges associated with the production of this content, have been found during the study. The results of our evaluation indicate that the system can provide the students with novel interaction mechanisms, to improve their skills, and it can be used as a complementary tool for the teaching and learning methods currently used in their education process.

The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving high performance, i.e., high accuracy and low response time. The present paper researches the design space and the impact of approaches of statistical and machine learning on accuracy and response time in human movement assessment. Results show that a random forest regression approach outperforms linear regression, support vector regression and neuronal network approaches. Since the results do not rely on the movement specifics, they can help improving the performance of automated human movement assessment, in general.

Background: Mobility and balance is essential for older adults' well-being and independence and the ability to maintain physically active. Early identification of functional impairment may enable early risk-of-fall assessments and preventive measures. There is a need to find new solutions to assess functional ability in easy, efficient, and accurate ways, which can be clinically used frequently and repetitively. Therefore, we need to understand how functional tests and expert assessments (EAs) correlate with new techniques.Objective: To explore whether the skeleton avatar technique (SAT) can predict the results of functional tests (FTs) of mobility and balance: Timed Up and Go (TUG), the 30-s chair stand test (30sCST), the 4-stage balance test (4SBT), and EA scoring of movement quality.Methods: Fifty-four older adults (+65 years) were recruited through pensioners' associations. The test procedure contained three standardized FTs: TUG, 30sCST, and 4SBT. The test performances were recorded using a three-dimensional SAT camera. EA scoring was performed based on the video recordings of the 30sCST. Functional ability scores were aggregated from balance and mobility scores. Probability theory-based statistical analyses were used on the data to aggregate sets of individual variables into scores, with correlation analysis used to assess the dependency between variables and between scores. Machine learning techniques were used to assess the appropriateness of easily observable variables/scores as predictors of the other variables included.Results: The results indicate that SAT data of the fourth 4SBT stage could be used to predict the aggregated results of all stages of 4SBT (with 7.82% mean absolute error), the results of the 30sCST (11.0%), the TUG test (8.03%), and the EA of the sit-to-stand movement (8.79%). There is a moderate (significant) correlation between the 30sCST and the 4SBT (0.31, p = 0.03), but not between the EA and the 30sCST.Conclusion: SAT can predict the results of the 4SBT, the 30sCST (moderate accuracy), and the TUG test and might add important qualitative information to the assessment of movement performance in active older adults. SAT might in the future provide the means for a simple, easy, and accessible assessment of functional ability among older adults.

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Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners’ interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the student’s learning style, study schedule, knowledge, and performance. Quizzing might be used to help to create individualized/personalized spaced repetition algorithm in order to improve long-term retention of knowledge and provide efficient learning in online learning platforms. Current spaced repetition algorithms have pre-defined repetition rules and parameters that might not be a good fit for students’ different learning styles in online platforms. This study uses different machine learning models and a rich context model to analyze quizzing and reading records from e-learning platform called Hypocampus in order to get some insights into the relevant features to predict learning outcome (quiz answers). By knowing the answer correctness, a learning system might be able to recommend personalized repetitive schedule for questions with maximizing long-term memory retention. Study results show that question difficulty level and incorrectly answered previous questions are useful features to predict the correctness of student’s answer. The gradient-boosted tree and XGBoost models are best in predicting the correctness of the student’s answer before answering a quiz. Additionally, some non-linear relationship was found between the reading learning material behavior in the platform and quiz performance that brings added value to the accuracy for all used models.