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.
An off-shoot of the infamous “How to Make (Almost) Anything” course at the Massachusetts Institute of Technology, “How to Grow (Almost) Anything” tackles the core concepts behind designing with biology – prototyping biomolecules, engineering biological computers, and 3D printing biomaterials. The material touches elements of synthetic biology, ethics of biotechnology, protein design, microfluidic fabrication, microbiome sequencing, CRISPR, and gene cloning.
In a similar fashion to the original HTMAA course, HTGAA works by introducing a new concept each week that builds up to a final project. Students learn about designing DNA experiments, using synthesized oligonucleotide primers to amplify a PCR product, testing the impact of genes on the production of lycopene in E coli., protein analysis and folding, isolating a microbiome colony from human skin and confining bacteria to image, printing 3D structures that contain living engineered bacteria, and using expansion microscopy (ExM) to visualize a mouse brain slice. The final projects run the gamut from creating a biocomputer in a cream to isolating yeast from bees.
Growing out from an initiative to create large communities around biotechnology research, the course requires minimal prior exposure to biology. By working directly with hands-on applications to biodesign concepts, students are able to direct apply their knowledge of theoretical biology concepts to real-world applications, making it an ideal springboard for bio-inspired DIY projects. Even though the syllabus isn’t fully available online, there’s a treasure trove of past projects to browse through for your next big inspiration.
While DNA-based computing may not be taking over silicon quite so soon, there is progress in the works. In a paper published by Small, researchers from the University of Rochester demonstrate a molecular computing system capable of calculating square roots of integers up to 900. The computer is built from synthetic biochemical logic gates using hybridization, a process where two strands of DNA join to form double-stranded DNA, and strand displacement reactions.
DNA-based circuits have already been shown to implement complex logic functions, but most existing circuits prior to the recent paper were unable to calculate square root operations. This required 4-bit binary numbers – the new prototype implements a 10-bit square root logic circuit, operating up to the decimal integer 900.
The computer uses 32 strands of DNA for storing and processing information. The process uses three modules, starting off with encoding a number on the DNA. Each combination is attached to a florescent marker, which changes signal during hybridization in the second module. The process for calculating the square root controls the signals, with the results deducted from the final color according to a threshold set in the third module.
We’re beginning to see the end of Moore’s Law approaching, with companies like Intel and AMD struggling to shrink transistors 10 nm wide. Nevertheless, with DNA molecules still about 10 time smaller than the best transistors today and DNA computing systems continuing to gain in sophistication, biochemical circuits could potentially be holding solutions to increasing the speed of computing beyond silicon computing.
In New Orleans, a Loyola University professor has been creating original art out of glow-in-the-dark fish gut bacteria, enough to fill 1000 Petri dishes. Her first major foray into art was biomorphic abstractions, inspired by Impressionist painters, with her current work reflecting much of the abstraction of the earlier style.
The bacteria comes from the Pacific Rock Fish and glows a vibrant electric-blue. It is typically kept in a freezer and has a texture and color similar to water when it’s being used. The luminescence only lasts for 24 hours, presenting timing challenges when preparing artwork for a photoshoot, as artist [Hunter Cole] often does. With a Q-tip, [Cole] paints roses, lilies, and insects onto the Petri dishes and arranges them for surreal photography shoots. In addition to painting shapes in agar, she uses a light painting technique by filling clear water bottles with the bacteria for long-exposure shots.
[Cole] is planning on presenting her work at an art exhibit in New Orleans, along with showcasing a performance piece featuring models clad in chandelier-like costumes glowing with bioluminescent bacteria in petri dishes.
Working in a university or research laboratory on interesting, complicated problems in the sciences has a romanticized, glorified position in our culture. While the end results are certainly worth celebrating, often the process of new scientific discovery is underwhelming, if not outright tedious. That’s especially true in biology and chemistry, where scaling up sample sizes isn’t easy without a lot of human labor. A research group from Reading University was able to modify a 3D printer to take some of that labor out of the equation, though.
This 3D printer was used essentially as a base, with the printing head removed and replaced with a Raspberry Pi camera. The printer X/Y axes move the camera around to all of the different sample stored in the print bed, which allows the computer attached to the printer to do most of the work that a normal human would have had to do. This allows them to scale up massively and cheaply, presumably with less tedious inputs from a large number of graduate students.
While the group hopes that this method will have wide applicability for any research group handling large samples, their specific area of interest involves researching “superbugs” or microbes which have developed antibiotic resistance. Their recently-published paper states that any field which involves bacterial motility, colony growth, microtitre plates or microfluidic devices could benefit from this 3D printer modification.
Join us on Wednesday 5 June 2019 at noon Pacific for the Disrupting Cell Biology Hack Chat with Incuvers!
A lot of today’s most successful tech companies have creation myths that include a garage in some suburban neighborhood where all the magic happened. Whether there was literally a garage is not the point; the fact that modest beginnings can lead to big things is. For medical instrument concern Incuvers, the garage was actually a biology lab at the University of Ottawa, and what became the company’s first product started as a simple incubator project consisting of a Styrofoam cooler, a space blanket, and a Soda Stream CO2 cylinder controlled by an Arduino.
From that humble prototype sprang more refined designs that eventually became marketable products, setting the fledgling company on a course to make a huge impact on the field of cell biology with innovative incubators, including one that can image cell growth in real time. What it takes to go from prototype to product has been a common theme in this year’s Hack Chats, and Noah, Sebastian, and David from Incuvers will drop by Wednesday to talk about that and more.
Our Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday June 5 at 12:00 PM Pacific time. If time zones have got you down, we have a handy time zone converter.
Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about.
Eyes are windows into the soul, the old saying goes. They are also pathways into the mind, as much of our brain is involved in processing visual input. This dedication to vision is partly why much of AI research is likewise focused on machine vision. But do artificial neural networks (ANN) actually work like the gray matter that inspired them? A recently published research paper (DOI: 10.1126/science.aav9436) builds a convincing argument for “yes”.
Neural nets were named because their organization was inspired by biological neurons in the brain. But as we learned more and more about how biological neurons worked, we also discovered artificial neurons aren’t very faithful digital copies of the original. This cast doubt whether machine vision neural nets actually function like their natural inspiration, or if they worked in an entirely different way.
This experiment took a trained machine vision network and analyzed its internals. Armed with this knowledge, images were created and tailored for the purpose of triggering high activity in specific neurons. These responses were far stronger than what occurs when processing normal visual input. These tailored images were then shown to three macaque monkeys fitted with electrodes monitoring their neuron activity, which picked up similarly strong neural responses atypical of normal vision.
Manipulating neural activity beyond their normal operating range via tailored imagery is the Hollywood portrayal of mind control, but we’re not at risk of input injection attacks on our brains. This data point gives machine learning researchers confidence their work still has relevance to biological source material, and neuroscientists are excited about the possibility of exploring brain functions without invasive surgical implants. Artificial neural networks could end up help us better understand what happens inside our brain, bringing the process full circle.
[via Science News]