Key result
An embedded Convolutional Neural Network executed directly on an IoT device achieved up to 97% accuracy for ECG analysis while using less than 200 mW and processing over 300 heartbeats per second.
Why the study?
IoT systems using AI for sensor data processing typically depend on cloud connectivity or are restricted to simplified models.
An optimized embedded CNN on an IoT device can perform highly accurate, low-power, and fast local ECG analysis without relying on continuous cloud connectivity.
Facilitates real-time on-device ECG analysis without cloud reliance; leaves open clinical validation before diagnostic adoption.
IoT systems that employ AI and neural networks for processing sensor data are usually dependent on an active connection to the cloud, or restricted to simplified models and techniques. By employing a highly optimised Convolutional Neural Network that can be executed directly on our heterogeneous IoT device, we can address both these problems without sacrificing accuracy. Additionally, leveraging the cloud when unknown or uncertain samples are seen allows us to continuously improve the model. Capable of running a “normal” model trained using frameworks such as TensorFlow, we show that our system can achieve accuracy of up to 97% in medical applications such as ECGs. This is done using our device that uses less than 200 mW but can locally process more than 300 heart beats per second.
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Burger et al. (2020) studied ECG analysis. Embedded Convolutional Neural Network on IoT device was evaluated on Accuracy in medical applications such as ECGs. An embedded Convolutional Neural Network executed directly on an IoT device achieved up to 97% accuracy for ECG analysis while using less than 200 mW and processing over 300 heartbeats per second.
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