Proactive Wellness via Digital Twins: Forecasting Fatigue Crashes Using LSTM Networks on Physiological Time-Series Signals

Proactive wellness via digital twins: forecasting fatigue crashes with LSTM networks on physiological time-series signals.

Proactive wellness via digital twins: forecasting fatigue crashes with LSTM networks on physiological time-series signals. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.