Abstract Background: Patients with axial spondyloarthritis (axSpA) benefit from regular home-based exercise (HbE). In spite of recommendations, a relevant proportion German axSpA patients does not adhere to recommended HbE practices. To enhance care, we developed the novel digital therapeutic (DTx) “Axia” compliant European medical device regulation (MDR). Axia offers modern app-based solution patient educative content and further integrated features. Objective: We aimed assess Axia’s...

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied across diverse domains. However, progress is impeded by scarcity training data due to expensive acquisition and privacy legislation. Synthetic emerges as solution, but abundance released models limited overview literature pose challenges for decision-making. This work surveys 417 Data Generation (SDG) over last decade, providing comprehensive model types, functionality, improvements. Common attributes are...

Abstract Synthetic data generation describes the process of learning underlying distribution a given real dataset in model, which is, turn, sampled to produce new objects still adhering original distribution. This approach often finds application where circumstances limit availability or usability real-world datasets, for instance, health care due privacy concerns. While image synthesis has received much attention past, time series are key many practical (e.g., industrial) applications. To...

A core selling point of application containers is their fast start times compared to other virtualization approaches like virtual machines. Predictable and container are crucial for improving guaranteeing the performance containerized cloud, serverless, edge applications. While previous work has investigated starts, there remains a lack understanding how may vary across configurations. We address this shortcoming by presenting analyzing dataset approximately 200,000 open-source Docker Hub...

Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with application artificial intelligence to sports research, coaching optimization athletic performance. The strength AI regards applied performance involve automation time-consuming tasks, processing large amounts data, recognition complex patterns relationships. However, it is also essential be aware weaknesses integration into this field. For instance, imperative that...

Aim: To characterize the impact of German strategy for containment Coronavirus SARS-CoV-2 (social distancing and lockdown) on training, other habitual physical activity, sleep in highly trained kayakers canoeists. Method: During four weeks immediately prior to following beginning government's March 23, 2020, 14 athletes (VO2peak: 3162±774 ml/min; 500-m best time: 117.9±7.9 s) wore a multi-sensor smartwatch allow continuous assessment heart rate, duration. Result: In comparison before...

Abstract Training studies in elite athletes traditionally focus on the relationship between scheduled training (TRAIN) and performance. Here, we added activities outside of i.e., off-training (OFF) contributing to total (TOTAL) evaluate contribution OFF Eight rowers recorded TRAIN during waking hours for one season (30–45 weeks) with multisensory smartwatches. Changes performance were assessed via rowing ergometer testing maximum oxygen uptake ( $${\dot{\text{V}}}$$ <mml:math...

The present study was designed to assess the psycho-physiological responses of physically untrained individuals mobile-based multi-stimulating, circuit-like, multiple-joint conditioning (CircuitHIIT) performed either once (1xCircuitHIIT) or twice (2xCircuitHIIT) daily for four weeks. In this single-center, two-arm randomized, controlled study, 24 men and women (age: 25 ± 5 years) first received no training instructions weeks then 4 1xCircuitHIIT 2xCircuitHIIT (5 7 in each group) daily....

Background Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in continuous monitoring vital signs. So far, these devices have primarily been used for sports. Objective However, using technologies health care, further validations measurement accuracy hospitalized patients are essential but lacking to date. Methods We conducted prospective validation study with 201 after moderate major surgery controlled setting benchmark heart rate measurements 4...

Research results on the training intensity distribution (TID) in endurance athletes are equivocal. This non-uniformity appears to be partially founded different quantification methods that implemented. So far, TID research has solely focused sports involving lower-body muscles as prime movers (e.g. running). Sprint kayaking imposes high demands upper-body capacity of athlete. As there structural and physiological differences between upper- musculature, should dominant sports. Therefore, we...

Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently consumer-grade fitness trackers could-at least theory-help to fill this gap, but benchmarks on applicability and accuracy these technologies currently...

Abstract Purpose Pronounced differences in individual physiological adaptation may occur following various training mesocycles runners. Here we aimed to assess the changes performance and of recreational runners performing with different intensity, duration frequency. Methods Employing a randomized cross-over design, intra-individual responses [i.e., peak ( $${\dot{\text V}}{\text O}_{2 {\rm peak}}$$ <mml:math...

Exercise training in heart failure (HF) is recommended but not routinely offered, because of logistic and safety-related reasons. In 2020, the German Society for Prevention&Rehabilitation Cardiology requested establishing dedicated "HF groups." Here, we aimed to implement evaluate feasibility safety one first HF groups Germany.Twelve patients (three women) with symptomatic (NYHA class II/III) an ejection fraction ≤ 45% participated were offered weekly, physician-supervised exercise 1 year....

Time series data are widely used and provide a wealth of information for countless applications. However, some applications faced with limited amount data, or the cannot be due to confidentiality concerns. To overcome these obstacles, time can generated synthetically. For example, electrocardiograms synthesized make them available building models predict conditions such as cardiac arrhythmia without leaking patient information. Although many different approaches synthesis have been proposed,...

This paper compares the performance of R, Python, and Rust in context data processing tasks. A real-world task form an aggregation benchmark measurement results was implemented each language, their execution times were measured. The indicate that while all languages can perform tasks effectively, there are significant differences performance. Even same code showed runtime depending on interpreter used for execution. Python most efficient, with R requiring much longer times. Additionally,...

In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution are precious, they can provide detailed information for specific area. However, measurements with high resolution time-consuming and costly. Only few approaches exist create high-resolution extensive areas. This paper explores extract nDSMs from low-resolution Sentinel-2 data, allowing us derive models. We thereby utilize...

<title>Abstract</title> Synthetic data generation describes the process of learning underlying distribution a given real dataset in model, which is, turn, sampled to produce new objects still adhering original distribution. This approach often finds application where circumstances limit availability or usability real-world datasets, for instance, health care due privacy concerns. While image synthesis has received much attention past, time series are arguably even more relevant many...

Purpose: To evaluate retrospectively the training intensity distribution (TID) among highly trained canoe sprinters during a single season and to relate TID changes in performance. Methods: The heart rates on-water by 11 German sprint kayakers (7 women, 4 men) one male canoeist were monitored preparation periods (PP) 1 2, as well period of competition (CP) (total monitoring period: 37 weeks). zones (Z) defined Z1 [<80% peak oxygen consumption (VO2peak)], Z2 (81-87% VO2peak) Z3 (>87%...

Time series analysis remains a major challenge due to its sparse characteristics, high dimensionality, and inconsistent data quality. Recent advancements in transformer-based techniques have enhanced capabilities forecasting imputation; however, these methods are still resource-heavy, lack adaptability, face difficulties integrating both local global attributes of time series. To tackle challenges, we propose new architectural concept for based on introspection. Central this is the...

This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these shall be used evaluate different machine learning models design a deep architecture predict RPE in highly trained/national level soccer players. From dataset comprising 5402 training sessions 732 match observations, we gathered data on 174 distinct parameters, encompassing heart rate, GPS, accelerometer (Borg's 0-10 scale) 26...

Abstract Background Patients with axial spondyloarthritis (axSpA) benefit from regular home-based exercise (HbE). In spite of recommendations, a relevant proportion German axSpA patients does not adhere to recommended HbE practices. To enhance care, we developed the novel digital therapeutic (DTx) “Axia” compliant European medical device regulation (MDR). Axia offers modern app-based solution patient educative content and further integrated features. A first survey involved who assessed...

In many areas of decision-making, forecasting is an essential pillar. Consequently, different methods have been proposed. From our experience, recently presented are computationally intensive, poorly automated, tailored to a particular data set, or they lack predictable time-to-result. To this end, we introduce Telescope, novel machine learning-based approach that automatically retrieves relevant information from given time series and splits it into parts, handling each them separately....