Read Heart Rate Pulses with Windows 10 IoT Core on Raspberry Pi – A Step‑by‑Step Guide

Overview

This tutorial walks you through connecting a SparkFun Heart Rate Pulse Sensor to a Raspberry Pi 3 Model B and capturing its analog output using a UWP application on Windows 10 IoT Core. By the end, you’ll have a working example that reads real‑time heart‑beat data and can be extended to IoT dashboards or cloud services.

Step 1 – Wiring the Hardware

The pulse sensor has three pins:

• BLACK – Ground
• RED – 3 V / 5 V supply
• PURPLE – Analog signal output

Because the Raspberry Pi has no native analog inputs, we use the MCP3008 8‑channel ADC. Wire the sensor’s signal pin to channel 0 of the MCP3008, connect the sensor’s power pin to the Pi’s 3.3 V rail (or 5 V if the sensor supports it), and tie the ground pins together. The MCP3008 communicates over SPI, so connect its CS, SCK, SDI, and SDO pins to the Pi’s corresponding SPI pins. A full wiring diagram is provided in the schematic below.

Step 2 – Reading the Data in a UWP App

We’ve created a lightweight C# UWP project that opens the SPI bus, polls channel 0 of the MCP3008, and converts the raw value into a pulse rate. The code is open‑source on GitHub, so you can inspect, modify, or reuse it in your own IoT solutions. Key points:

• Initialize the SpiDevice with the correct bus ID and chip‑select line.
• Send the MCP3008 command byte and read the 10‑bit result.
• Map the raw ADC value to a heart‑beat frequency using a simple calibration curve.
• Display the instantaneous heart rate in the UI.

For a line‑by‑line walk‑through, refer to the README in the repository.

Step 3 – Extending the Demo

With the raw pulse data now available on the Pi, you can pursue several next steps:

• Real‑time visualization – Hook the UWP app to a local screen or embed a chart component. Libraries such as Syncfusion SfChart or Telerik UWP Chart make it trivial to plot heart‑rate traces.
• Cloud telemetry – Send the readings to Azure IoT Hub or another MQTT broker, then use Power BI or Grafana for advanced dashboards.
• Edge analytics – Apply on‑board filtering or anomaly detection to alert when the heart rate deviates from expected ranges.

These extensions illustrate how a single sensor can become the foundation of a full IoT health monitoring system.