Early Warning System: Detecting Flu & Infections using LSTM on Your Wrist ⌚️🔬
Have you ever woken up feeling slightly "off," only to find yourself down with a full-blown fever 24 hours later? What if your smartwatch could have warned you yesterday? 🌡️ In the world of predictive healthcare , your heart rate isn't just a number—it’s a time-series goldmine. By utilizing Deep Learning for Health and physiological signal processing , we can detect subtle shifts in your Resting…
In the realm of predictive healthcare, wearable devices like smartwatches can serve as early warning systems for infections and flu by monitoring physiological signals. By analyzing Resting Heart Rate (RHR) through a many-to-one LSTM architecture, the system can predict future heart rates based on historical data. The LSTM model is optimized for edge AI deployment, allowing it to run locally on the wearable device, thus ensuring privacy and real-time alerts.
The architecture begins with a photoplethysmogram (PPG) sensor that captures heart rate data. This raw data undergoes noise filtering and peak detection before being aggregated into daily RHR values, which are then fed into a 7-day sliding window context. This input is processed by the LSTM inference engine to generate a prediction of the next heart rate value.
The predicted value is compared to the actual heart rate to determine if it deviates beyond two standard deviations. If it does, an infection risk alert is triggered and displayed on a dashboard or sent as a notification to the user.
To implement this system, several prerequisites are needed, including a tech stack of Python (Keras/TensorFlow), NumPy, and C++ for the edge runtime. Basic knowledge of TensorFlow Lite Micro's interaction with ARM Cortex-M processors is also required. The dataset used should consist of heart rate time-series data, such as from the PMData or MyHeart Counts datasets.
The first step involves building a baseline model with LSTM layers to remember typical recovery patterns in physiological signals. The model is compiled with the Adam optimizer and mean absolute error (MAE) loss function. Due to memory constraints on smartwatches, the model is converted to a quantized version using post-training quantization, reducing the model size from 50MB to 8-bit integers without significant accuracy loss.
Finally, the quantized model is implemented on the microcontroller using TensorFlow Lite Micro's C++ library. The model is initialized, memory areas are set up, input data is provided for inference, and the predicted heart rate is compared to the actual value to determine if an alert should be sent.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
