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A smart cardio-health assistant based on Sparkfun Artemis ATP

Artemis is the flagship microprocessor module, developed by Sparkfun that will be integrated into the next generation of efficient development boards. Based on Cortex-M4F with BLE 5.0 capability along with 96MHz peak operation, with as low power as 6uA per MHz (less than 5mW). The Apollo3 chip inside each Artemis module can be powered from a single coin-cell battery. The SparkFun Artemis Module is fully FCC/IC/CE certified. Moreover, it provides support for AI/ML development through TensorFlow Lite.

Hence, utilizing the capabilities of Artemis ATP(All the Pins) board; with similar form-factor to the famous Arduino MEGA2560, I have developed a Smart Health Assistant that would provide the vital signals associated with cardiac health like Heart-Rate measurement, SPo2 and HRV measurement, Single channel ElectroCardioGram measurement along with ambient environment parameters: temperature, CO2 and TVOCs, humidity and associated altitude, that altogether will be augmented with the captured bio-signals to improve the efficiency and efficacy of the entire system.

The device also features WS2812B LED Ring, with gestures almost similar to Amazon’s Alexa color-ring, that will be used for notifications, criticality, and sensor-updates to Raspberry Pi 3B+ based server. Moreover, the device utilizes the capabilities of on-board ‘Always_ON’ microphone, which has been exploited along with the TensorFlow Lite based micro-speech code, that will be used to wake the device from idle state, or to initiate the device for real-time measurements.

Shortnote…

Early detection of abnormal cardiac activity is vital to identify heart problems and avoid sudden cardiac death. It has been proved that people with similar heart conditions almost have similar electrocardiogram (ECG) signals. Arrhythmia detection has been heavily relied on observing morphological features of the ECG signals which are tedious and very time consuming. Automatic detection of arrhythmia is more preferable through the use of classifiers based on machine-learning, that can alltogether run on low consumption MCU.”

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