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Description: This is a wonderful evaluation board cooperatively designed by danjuliodesigns and SparkFun. The Heart Rate Monitor Interface (HRMI) is an intelligent peripheral device that converts the ECG signal from the Polar Heart Rate Monitor (HRM) into easy-to-use heart rate data. It implements a sophisticated algorithm for computing an average heart rate even with noisy or intermittent data from the transmitter.
Note: This product is a collaboration with Dan Julio. A portion of each sales goes back to them for product support and continued development.
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In addition to working on humans, the Polar T31 transmitter can be strapped to a cow. We hung this interface on a steel bar about 2 feet from the cow with the transmitter, and it picked up the signal just fine (after we slathered some electrolytic gel on the transmitter). The data was collected by a simple serial program running on Ubuntu which sent a “G” command about once a second. If this pickup can work in a barn, it can pretty much work anywhere.
I used this device to get my heart rate to be displayed on two neopixle strips on my bicycle. The device was easy to use and I was up and running in a mater of minutes. The orientation of the device to the chest strap is important to get a robust signal. All my buddies got a kick out of this.
Easy installation. Plug in a usb cable (install FTDI Virtual COM Port Driver (VCP)), open serial terminal, type G1<enter> and you get the latest heart rate.
Remove solder jumper on SJ1, add solder jumper on OP0 and you can communicate via I2C (address 127 by default)
Only glitch I can replicate every time is by moving the sender out of range from the receiver. The values go berserk after that until I do a power recycle.
I attached the HRM to a Chromebook (Linux Mint 18 Cinnamon 64-bit) through the USB port. I observed the system log to get the device name (/dev/ttyUSB0) and used minicom 2.7 to verify that I send commands to the HRM and get data back from the HRM. I wrote Python code to periodically query the HRM and log the heart rate data and then tested it during an hour-long session on a treadmill. It worked great and the data was similar to the HR data shown on the treadmill. The whole experience took about three hours get to HR data. Next step is to optimize the code, add features to the code and understand the HR algorithm better. It was a challenging exercise and a good project for a beginner like me!