Embedded Sensing Group ESG Institute of Computer Science in V orarlberg ICV, University of St. Gallen HSG, Switzerland E-mail: leonardo.rossi@student.unisg.ch, Abstract--Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that significantly influence recovery and injury susceptibility. We introduce a novel synthetic data generation framework tailored explicitly for triathlon. This framework generates physiologically plausible athlete profiles, simulates individualized training programs that incorporate periodization and load-management principles, and integrates daily-life factors such as sleep quality, stress levels, and recovery states. We evaluated machine learning models (LASSO, Random Forest, and XGBoost) showing high predictive performance (AUC up to 0.86), identifying sleep disturbances, heart rate variability, and stress as critical early indicators of injury risk. This wearable-driven approach not only enhances injury prediction accuracy but also provides a practical solution to overcoming real-world data limitations, offering a pathway toward a holistic, context-aware athlete monitoring. Triathlon is a demanding multi-sport discipline that combines swimming, cycling, and running.

Garmin has some of the best proprietary fitness software around. Here's how to interpret all that meticulously gathered data. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. So, you've got a shiny new Garmin watch .

Today's keynote for Apple's Worldwide Developers Conference teased a lot of what users can expect later this year when all of its major software updates roll out. Big changes coming to iOS 18, macOS Sequoia and watchOS 11 include RCS support, a new Passwords app, a revamped Calculator app and a bunch of artificial intelligence (AI) infusions across the board thanks to the new "Apple Intelligence" system. If you weren't able to catch the news live, here's a rundown of everything announced at WWDC 2024. Apple revealed its plans to incorporate AI into its operating systems at WWDC this year. Dubbed "Apple Intelligence," this new generative AI system will appear in iOS and iPad 18 and macOS Sequoia in the form of (what Apple believes to be) practical tools that most people can use regularly.

In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich, longitudinal data for personal health monitoring. Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data. We created and curated three datasets that test 1) production of personalized insights and recommendations from sleep patterns, physical activity, and physiological responses, 2) expert domain knowledge, and 3) prediction of self-reported sleep outcomes. For the first task we designed 857 case studies in collaboration with domain experts to assess real-world scenarios in sleep and fitness. Through comprehensive evaluation of domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not statistically different from expert performance in fitness and, while experts remain superior for sleep, fine-tuning PH-LLM provided significant improvements in using relevant domain knowledge and personalizing information for sleep insights. We evaluated PH-LLM domain knowledge using multiple choice sleep medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on fitness, exceeding average scores from a sample of human experts. Finally, we trained PH-LLM to predict self-reported sleep quality outcomes from textual and multimodal encoding representations of wearable data, and demonstrate that multimodal encoding is required to match performance of specialized discriminative models. Although further development and evaluation are necessary in the safety-critical personal health domain, these results demonstrate both the broad knowledge and capabilities of Gemini models and the benefit of contextualizing physiological data for personal health applications as done with PH-LLM.

The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care, and everyday life even. This work reports about application of some machine learning methods (linear regression, linear discriminant analysis, k-nearest neighbors, decision tree, random forest, Gaussian naive Bayes, support-vector machine) for monitoring energy expenditures in athletes. The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i.e by features of the post-exercise load. The results obtained shown that the post-exercise heart activity features preserve the information of the in-exercise training loads and allow us to predict their actual in-exercise levels. The best performance can be obtained by the random forest classifier with all 8 heart rate features (micro-averaged area under curve value AUCmicro = 0.87 and macro-averaged one AUCmacro = 0.88) and the k-nearest neighbors classifier with 4 most important heart rate features (AUCmicro = 0.91 and AUCmacro = 0.89). The limitations and perspectives of the ML methods used are outlined, and some practical advices are proposed as to their improvement and implementation for the better prediction of in-exercise energy expenditures.

Do you want to see more videos on the channel? Let us know what you think, and what you need! 3. Overview Loud ML 1.5 feature set in the roadmap DISCLAIMER: The following information is being shared in order to outline some of our current product plans, but like everything else in life, even the best laid plans get put to rest. We are hopeful that the following can shed some light on our roadmap, but it is important to understand that it is being shared for INFORMATIONAL PURPOSES ONLY, and not as a binding commitment. Please do not rely on this information in making purchasing decisions because ultimately, the development, release, and timing of any products, features or functionality remains at our sole discretion, and is subject to change. Old training data is lost.

To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from elite athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day (rolling average, exponentially weighted moving average, acute:chronic workload ratio, monotony and strain). Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC$<$0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Learning curves suggested logistic regression was underfitting the load-injury relationship and that using a more complex model or increasing the amount of model building data may lead to future improvements. Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting they are limited as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training data may lead to the development of improved predictive models for injury prevention.

Using machine learning to predict (& prevent!) injuries in endurance athletes: Part 1 Alan Couzens, M.Sc. I've talked about different ways that we can assess the individual'dose- response' relationship or, more specifically, how we can work out just "what it takes" for a given athlete to reach a given performance level. I have suggested that, given recent advances in machine learning, the current models are largely out-dated and that we can find more accuracy in models that look at the independent impact of volume & intensity rather than wrapping these variables into one'training stress' metric. But there is another addition to the current performance models that is far more important and has the potential to be even more powerful in its application than load- fitness modeling: Turning the focus of our models to those things that prevent us from ultimately doing more load! This is the flipside of the'more is better' dose- response model.