There are a few different ways to take a person’s pulse, with varying utility depending on the categories said patient fits in to. [Nitin Nair]’s method doesn’t really have a medical application, but it’s certainly a neat example of what you can do with modern sensors.
The build combines an EmotiBit sensor platform with an Adafruit Feather and accompanying Charlieplexed LED module. The EmotiBit packs a PPG, or photoplethysmogram sensor, otherwise known as a pulse oximeter, which uses optical methods to detect changes in blood volume beneath the skin. From this data, a pulse rate can be derived, and the LEDs flashed with a heart graphic in concert with the rhythm of the wearer’s heart. The benefit of the PPG in the EmotiBit is that it can be worn on the wearer’s arm, or other location with suitable vascularization. This allows the wearer to place the sensor on the arm, and thus wear their heart on their sleeve.
It’s a cool concept, and we’d love to see it neatly packaged with a smoothly animated fade as a sports accessory. It’d be an easy way to signal how fast your heart rate recovers on a run with friends – the device could brag about your fitness for you. Alternatively, if pulse oximetry isn’t enough for you, go ahead and build an ECG instead!
With wearables still trying to solidify themselves in the consumer health space, there are a number of factors to consider to improve the reliability of such devices in monitoring biometrics. One of the most critical such parameters is the sampling rate. By careful selection of this figure, developers can minimize errors in the measurement, preserve power, and reduce costs spent on data storage. For this reason, [Brinnae Bent] and [Dr. Jessilyn Dunn] wanted to determine the optimal sampling rate for wrist-worn optical heart rate monitors. We’ve shared their earlier paper on analyzing the accuracy of consumer health devices, so they’ve done a lot of work in this space.
The results of their paper probably don’t surprise anyone. The lower the sampling rate, the lower the accuracy of the measurement, and the higher the sampling rate the more accurate the measurement when compared to the gold standard electrocardiogram. They also found that metrics such as root mean square of successive differences (RMSSD), used for calculating heart rate variability, requires sampling rates greater than 64 Hz, the nominal sampling rate of the wearable they were investigating and of other similar devices. That might suggest why your wearable is a bit iffy when monitoring your sleeping habits. They even released the source code for their heart rate variability analysis, so there’s a nice afternoon read if you were looking for one.
What really stood out to us about their work is how they thoroughly backed up their claims with data. Something crowdfunding campaigns could really learn from.
Wearables are ubiquitous in today’s society. Such devices have evolved in their capabilities from step counters to devices that measure calories burnt, sleep, and heart rate. It’s pretty common to meet people using a wearable or two to track their fitness goals. However, a big question remains unanswered. How accurate are these wearable devices? Researchers from the Big Ideas Lab evaluated a group of wearables to assess their accuracy in measuring heart rate.
Unlike other studies with similar intentions, the Big Ideas Lab specifically wanted to address whether skin color had an effect on the accuracy of the heart rate measurements, and an FDA-cleared Bittium Faros 180 electrocardiogram was used as the benchmark. Overall, the researchers found that there was no difference in accuracy across skin tones, meaning that the same wearable will measure heart rate on a darker skin-toned individual the same as it would on a lighter skin-toned. Phew!
However, that may be the only good news for those wanting to use their wearable to accurately monitor their heart rate. The researchers found the overall accuracy of the devices relative to ECG was a bit variable with average errors of 7.2 beats per minute (BPM) in the consumer-grade wearables and 13.9 BPM in the research-grade wearables at rest. During activity, errors in the consumer-grade wearables climbed to an average of 10.2 BPM and 15.9 in the research-grade wearables. It’s interesting to see that the research-grade devices actually performed worse than the consumer devices.
And there’s a silver lining if you’re an Apple user. The Apple Watch performed consistently better than all other devices with mean errors between 4-5 BPM during rest and during activity, unless you’re breathing deeply, which threw the Apple for a loop.
So, it seems as if wrist-worn heart rate monitors still have some work to do where accuracy is concerned. Although skin tone isn’t a worry, they all become less accurate when the subject is moving around.
If you’d like to try your own hand with fitness trackers, have a look at this completely open project, or go for the gold standard with a wearable DIY ECG.
It turns out that medical manufacturers also do hacking once in a while. [JanHenrikH] recently tweeted a photo of an ECG-Trigger-Unit that he’d opened up. Inside he found that the LCD screen was that of a Game Boy Advance (GBA) and the reason he could tell was that the screen’s original case was still there, complete with GAME BOY ADVANCE SP written on it.
In the manufacturer’s defense, this device was likely made around the year 2000 when gaming products were some of the best sources for high speed, high quality, small LCDs
displays. This design document for a portable ECG measurement instrument from as recently as 2013 cites reasons for using a GBA as:
- impressive plotting results,
- no serious transmission delays, and
- fine graphics processing capability.
The Verge had even turned up this US patent from 1997 that has the diagnostic medical device be a cartridge for plugging into a Game Boy. At the time, PCs were frequently used for medical displays but this patent cites issues such as the higher cost of PCs, software installation issues, and crashing. However, they talk about the crashing being due to running word processing and spreadsheet software on the same PC, something not likely to happen if the PC is dedicated to bedside monitoring.
But despite all those pros, wouldn’t you feel surprise and alarm when you first glimpse the Game Boy inside the device that’s monitoring your heart? We also have to wonder what licensing these products went through in the countries in which they were used. This particular device was made by German company Medical Imaging Electronics.
Game Boy hacks aren’t limited to the medical industry though. Here on Hackaday, we’ve seen them turned into remote controls for flying drones and we’ve seen Game Boy cartridge emulators that use STM32. Finally, if you’re wondering where you saw [Jan Henrik]’s name before, he was one of the two hackers driving the motorized armchair in a photo in our [Jenny List]’s SHACamp 2017 write-up.
Our thanks to [geonomad] for the tip!
The “Crivit Sports” is an inexpensive chest-strap monitor that displays your current pulse rate on a dedicated wristwatch. This would be much more useful, and presumably more expensive, if it had a logging option, or any way to export your pulse data to a more capable device. So [RoGeorge] got to work. Each post of the (so-far) three-part series is worth a read, not the least because of the cool techniques used.
In part one, [RoGeorge] starts out by intercepting the signals. His RF sniffer? An oscilloscope probe shorted out in a loop around the heart monitor. Being able to read the signals, it was time to decode them. Doing pushups and decoding on-off keyed RF signals sounds like the ideal hacker training regimen, but instead [RoGeorge] used a signal generator, clipped to the chest monitor, to generate nice steady “heartbeats” and then read the codes off the scope without breaking a sweat.
With the encoding in hand, and some help from the Internet, he tested out his hypothesis in part two. Using an Arduino to generate the pulses logged in part one, he pulsed a coil and managed to get the heart rates displayed on the watch.
Which brings us to part three. What if there were other secrets to be discovered? Brute-forcing every possible RF signal and looking at the watch to see the result would be useful, but doing so for 8,192 possible codes would drive anyone insane. So [RoGeorge] taught himself OpenCV in Python and pointed a webcam at the watch. He wrote a routine that detected the heart icon blinking, a sign that the watch received a valid code, and then transmitted all possible codes to see which ones were valid. Besides discovering a few redundant codes, he didn’t learn much new from this exercise, but it’s a great technique.
We’re not sure what’s left to do on the Crivit. [RoGeorge] has already figured out the heart-rate data protocol, and could easily make his own logger. We are sure that we liked his thorough and automated approach to testing it all, from signal-generator-as-heartbeat to OpenCV as feedback in a brute-force routine. We can’t wait to see what’s up next.
[Ashwin K Whitchurch] and [Venkatesh Bhat] Have not missed a beat entering this year’s Hackaday Prize with their possibly lifesaving gadget HeartyPatch. The project is a portable single wire ECG machine in a small footprint sporting Bluetooth Low Energy so you can use your phone or another device as an output display.
Projects like this are what the Hackaday Prize is all about, Changing the world for the better. Medical devices cost an arm and a leg so it’s always great to see medical hardware brought to the Open Source and Open Hardware scene. We can already see many uses for this project hopefully if it does what’s claimed we will be seeing these in hospitals around the world sometime soon. The project is designed around the MAX30003 single-lead ECG monitoring chip along with an ESP32 WiFi/BLE SoC to handle the wireless data transmission side of things.
We really look forward to seeing how this one turns out. Even if this doesn’t win a prize, It’s still a winner in our books even if it only goes on to help one person.
[Andrew Wilson] is a pretty extreme guy. He base jumps for fun, and is also a hacker. And while you can try to explain the awesome adrenaline rush that comes with this kind of extreme hobby, it’d be nice if you could show it off, you know, quantitatively. So, he decided to make his own EKG, pair it with his GoPro, and go for a jump!
An EKG is an electrocardiogram — a fancy term for a heart rate monitor — and [Andrew’s] has built his own using a small instrument amplifier circuit. This circuit amplifies the differential signal put out by your heart. The data are fed through an ADC on an Arduino Uno, and then saved to a SD card. He also added a piezo buzzer to try to help sync the data to the video — unfortunately it was too quiet for the GoPro to pick up. So for now he’s stuck with pressing record and start on his EKG at the same time.
Once he was satisfied with a few safe tests, he decided to take it for a base jump. For our viewing pleasure, he’s taken the data collected from the EKG and post-processed it into a nice scrolling graph overlay for the video.
We guarantee your hands will get sweaty as his heart rate goes up as he prepares to make the plunge.
Continue reading “Wearing A Homemade EKG Whilst Base Jumping!”