Researchers Propose LSTM-Based Smartwatch System to Detect Infections Before Symptoms

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A technical framework published on DEV Community outlines how Long Short-Term Memory (LSTM) neural networks can be deployed on wearable devices to predict illness onset by monitoring resting heart rate. The system analyzes seven days of heart rate data to establish a personal baseline and flags deviations greater than two standard deviations as potential infection alerts. To fit within the hardware constraints of smartwatches, the model is compressed using post-training quantization, converting 32-bit floats to 8-bit integers via TensorFlow Lite. All inference runs locally on the device using TensorFlow Lite Micro, meaning health data never leaves the wearable. The guide recommends publicly available datasets such as PMData or MyHeart Counts for training and targets deployment on ARM Cortex-M processors.

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