With all the attention given to heart rate monitoring and step counting, respiratory rate monitoring is often overlooked. Smartwatches are starting to incorporate respiratory rate monitoring more and more these days. However, current devices often simply look at breaths per minute without extracting more interesting features of the respiratory waveform which could give us more insight into our bodies than breaths per minute could alone. [Davies] and his team decided they wanted to change that by making an earbud that can measure respiratory rate. Continue reading “Breathe Through Your Ears?”
It seems like within the last ten years, every other gadget to be released has some sort of heart rate monitoring capability. Most modern smartwatches can report your BPMs, and we’ve even seen some headphones with the same ability hitting the market. Most of these devices use an optical measurement method in which skin is illuminated (usually by an LED) and a sensor records changes in skin color and light absorption. This method is called Photoplethysmography (PPG), and has even been implemented (in a simple form) in smartphone apps in which the data is generated by video of your finger covering the phone camera.
The basic theory of operation here has its roots in an experiment you probably undertook as a child. Did you ever hold a flashlight up to your hand to see the light, filtered red by your blood, shine through? That’s exactly what’s happening here. One key detail that is hard to perceive when a flashlight is illuminating your entire hand, however, is that deoxygenated blood is darker in color than oxygenated blood. By observing the frequency of the light-dark color change, we can back out the heart rate.
This is exactly how [Andy Kong] approached two methods of measuring heart rate from a webcam.
Method 1: The Cover-Up
The first detection scheme [Andy] tried is what he refers to as the “phone flashlight trick”. Essentially, you cover the webcam lens entirely with your finger. Ambient light shines through your skin and produces a video stream that looks like a dark red rectangle. Though it may be imperceptible to us, the color changes ever-so-slightly as your heart beats. An FFT of the raw data gives us a heart rate that’s surprisingly accurate. [Andy] even has a live demo up that you can try for yourself (just remember to clean the smudges off your webcam afterwards).
Method 2: Remote Sensing
Now things are getting a bit more advanced. What if you don’t want to clean your webcam after each time you measure your heart rate? Well thankfully there’s a remote sensing option as well.
For this method, [Andy] is actually using OpenCV to measure the cyclical swelling and shrinking of blood vessels in your skin by measuring the color change in your face. It’s absolutely mind-blowing that this works, considering the resolution of a standard webcam. He found the most success by focusing on fleshy patches of skin right below the eyes, though he says others recommend taking a look at the forehead.
Every now and then we see something that works even though it really seems like it shouldn’t. How is a webcam sensitive enough to measure these minute changes in facial color? Why isn’t the signal uselessly noisy? This project is in good company with other neat heart rate measurement tricks we’ve seen. It’s amazing that this works at all, and even more incredible that it works so well.
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.
It’s interesting to see the different form-factors that people utilize for their portable biometric sensors. We’re seeing heart rate monitors and other biometric sensors integrated into watches, earbuds, headbands, sports bras, and all sorts of other garments and accessories. [Gabi] took an intriguing approach, integrating an electrocardiogram (ECG) into a backpack. This type of heart rate project is pretty popular here on Hackaday, so it was great running across [Gabi’s] design during our daily perusing for the new and exciting.
[Gabi] used an Adafruit FLORA, a BLE module, an ECG sensor from Bitalino, a few other ancillary components, and, of course, a backpack. We appreciate that she walked us through the list of stumblingblocks she came across and how she got around them. So much of the time in our excitement to share our projects we remove the gory details and only present the finished project when really, we learn most from all the things that didn’t work more so than the things that did. Finally, [Gabi] walks through the intricacies of the threading and the particular placement of the snap connectors to attach the circuit to the ECG electrodes. Things get pretty tricky, but luckily [Gabi] documents her project pretty meticulously with schematics, pictures, and early notice of pitfalls.
[Gabi] made sure to remind her readers that this is a prototype, not a medical device. She also brought up electrical safety. Biometric devices such as ECGs need to include a strict set of isolation circuits to prevent potential harm to the user. Fortunately, there are a few well-characterized methods to accomplish this.
So thanks for a really cool project, [Gabi], and to our readers, why not enjoy some of our other ECG projects while you’re at it?
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.
Building a real-life version of the Star Trek tricorder has been the goal of engineers and hackers alike since the first time Dr McCoy complained about being asked to work outside of his job description. But while modern technology has delivered gadgets remarkably similar in function, we’ve still got a long way to go before we replicate 24th century Starfleet design aesthetic. Luckily there’s a whole world of dedicated hackers out there who are willing to take on the challenge.
[Taste The Code] is one such hacker. He wanted to build himself a practical gadget that looked like it would be at home on Picard’s Enterprise, so he gathered up the components to build a hand-held heart rate monitor and went in search for a suitable enclosure. The electronics were simple enough to put together thanks to the high availability and modularity we enjoy in a post-Arduino world, but as you might expect it’s somewhat more difficult to put it into a package that looks suitably sci-fi while remaining functional.
Internally his heart rate monitor is using an Arduino Pro Mini, a small OLED screen, and a turn-key pulse sensor which was originally conceived as a Kickstarter in 2011 by “World Famous Electronics”. Wiring is very simple: the display is connected to the Arduino via I2C, and the pulse sensor hooks up to a free analog pin. Everything is powered by 3 AA batteries delivering 4.5 V, so he didn’t even need a voltage regulator or the extra components required for a rechargeable battery pack.
Once everything was confirmed working on a breadboard, [Taste The Code] started the process of converting a handheld gyroscopic toy into the new home of his heart rate monitor. He kept the battery compartment in the bottom, but everything else was stripped out to make room. One hole was made on the pistol grip case so that a finger tip could rest on the pulse sensor, and another made on the side for the OLED screen. This lets the user hold the device in a natural way while getting a reading. He mentions the sensor can be a bid fiddly, but overall it gives accurate enough readings for his purposes.
If you’re more interested in the practical aspects of a real-life Star Trek tricorder we’ve seen several projects along those lines over the years, including a few that were entered into the Hackaday Prize.
Heart rate sensors available for DIY use employ photoplethysmography which illuminates the skin and measures changes in light absorption. These sensors are cheap, however, the circuitry required to interface them to other devices is not. [Petteri Hyvärinen] is successfully investigating the use of capacitive touchscreens for heart rate sensing among other applications.
The capacitive sensor layer on modern-day devices has a grid of elements to detect touch. Typically there is an interfacing IC that translates the detected touches into filtered digital numbers that can be used by higher level applications. [optisimon] first figured out a way to obtain the raw data from a touch screen. [Petteri Hyvärinen] takes the next step by using a Python script to detect time variations in the data obtained. The refresh rate of the FT5x06 interface is adequate and the data is sent via an Arduino in 35-second chunks to the PC over a UART. The variations in the signal are very small, however, by averaging and then using the autocorrelation function, the signal was positively identified as a pulse.
A number of applications could benefit from this technique if the result can be replicated on other devices. Older devices could possibly be recycled to become low-cost medical equipment at a fraction of the cost. There is also the IoT side of things where the heart-rate response to media such as news, social media and videos could be used to classify content.