CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning.

Goal: This paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals. Method: The system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis. Results: The system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application. Conclusion: The system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.

Artificial intelligence-based early detection of acute kidney injury after cardiac surgery.

OBJECTIVES: This study aims to improve the early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms. METHODS: Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modelling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 h after surgery. Demographic characteristics, comorbidities, preoperative cardiac status and intra- and postoperative variables including creatinine and haemoglobin values were retrieved for analysis. RESULTS: From 7507 patients analysed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 h with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9% and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and haemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease. CONCLUSIONS: The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 h after surgery with the best discriminatory characteristics reported so far.

A machine learning-based workflow for automatic detection of anomalies in machine tools.

Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

The Impact of the German Strategy for Containment of Coronavirus SARS-CoV-2 on Training Characteristics, Physical Activity and Sleep of Highly Trained Kayakers and Canoeists: A Retrospective Observational Study.

Aim: To characterize the impact of the German strategy for containment of Coronavirus SARS-CoV-2 (social distancing and lockdown) on the training, other habitual physical activity, and sleep in highly trained kayakers and canoeists. Method: During the 4 weeks immediately prior to and following the beginning of the German government's strategy for containment of Coronavirus SARS-CoV-2 on March 23, 2020, 14 highly trained athletes (VO2peak: 3,162 ± 774 ml/min; 500-m best time: 117.9 ± 7.9 s) wore a multi-sensor smartwatch to allow continuous assessment of heart rate, physical activity, and sleep duration. Result: In comparison to before lockdown, the overall weekly training time and the average length of each session of training during the lockdown decreased by 27.6% (P = 0.02; d = 0.91) and 15.4% (P = 0.36; d = 0.36), respectively. At the same time, the number of sessions involving specific (i.e., canoeing and kayaking) and non-specific (i.e., running, cycling) training, respectively, did not change (P = 0.36-0.37; d = 0.34-0.35). The number of sessions involving strength (+17.4%; P = 0.03; d = 0.89) or other types of training (+16.7%; P = 0.06; d = 0.75) increased during the lockdown with 2.8-17.5% more training time involving a heart rate <60%, 82-88, 89-93, or 94-100% of individual peak heart rate (HRpeak) (P = 0.03-0.86; d = 0.07-1.38), and 4.3-18.7% less time with a heart rate of 60-72 or 73-83% HRpeak (P = < 0.001-0.0.26; d = 0.44-2.24). The daily duration of sleep was ~30 min (6.7%) longer during the lockdown (P < 0.001; d = 1.53) and the overall time spent lying down was 17% greater (P < 0.001; d = 2.26); whereas sitting time (-9.4%; P = 0.003; d = 1.23), the duration of light (15 min; -7.3%; P = 0.04; d = 0.83), and moderate (-18.6%; P = 0.01; d = 1.00) physical activity other than training (-9.4%; P = 0.22; d = 0.00) were all lower during lockdown. Conclusion: The present data revealed that following the German lockdown for containment of the Coronavirus SARS-CoV-2, highly trained kayakers and canoeists spent less overall time training each week (-27.6%) with, on average, shorter training sessions (-15.1%) and less light-to-moderate physical activity outside of training. Moreover, they performed more strength training sessions per week, and all engaged in more training at intensities >82 and <60% of HRpeak and spent longer periods lying down and sleeping during the lockdown.