With CNC machines, getting the best results depends on knowing how fast your tool is moving relative to the workpiece. But entry-level CNC routers don’t often include a spindle tachometer, forcing the operator to basically guess at the speed. This DIY optical spindle tach aims to fix that, and has a few nice construction tips to boot.
The CNC router in question is the popular Sienci, and the 3D-printed brackets for the photodiode and LED are somewhat specific for that machine. But [tmbarbour] has included STL files in his exhaustively detailed write-up, so modifying them to fit another machine should be easy. The sensor hangs down just far enough to watch a reflector on one of the flats of the collet nut; we’d worry about the reflector surviving tool changes, but it’s just a piece of shiny tape that’s easily replaced. The sensor feeds into a DIO pin on a Nano, and a small OLED display shows a digital readout along with an analog gauge. The display update speed is decent — not too laggy. Impressive build overall, and we like the idea of using a piece of PLA filament as a rivet to hold the diodes into the sensor arm.
Want to measure machine speed but don’t have a 3D printer? No worries — a 2D-printed color-shifting tach can work too.
Continue reading “Optical Tach Addresses the Need for Spindle Speed Control”
You may not realize it, but how fast a person walks is an important indicator of overall health. We all instinctively know that we lag noticeably when a cold or the flu hits, but monitoring gait speed can help diagnose a plethora of chronic diseases and conditions. Wearables like Fitbit would be one way to monitor gait speed, but the Computer Science and Artificial Intelligence Lab at MIT thinks there’s a better way: a wireless appliance that measures gait speed passively.
CSAIL’s sensor, dubbed WiTrack (PDF), is a wall-mounted plaque that could be easily concealed as a picture or mirror. It sends out low-power RF signals between about 5- and 7-GHz to perform 3D motion tracking in real time. The WiTrack sensor has a resolution of about 8 cm at those frequencies. With their WiGait algorithms (PDF), the CSAIL team led by [Chen-Yu Hsu] is able to measure not only overall walking speed, but also stride length. That turns out to be critical to predicting the onset of such diseases as Parkinson’s, which has a very characteristic shuffling gait in the early phase of the disease. Mobility impairments from other diseases, like ALS and multiple sclerosis, could also be identified.
WiTrack builds on [Hsu]’s previous work with through-wall RF tracking. It’s nice to see a novel technique coming closer to a useful product, and we’ll be watching to see where this one goes.
Continue reading “Measuring Gait Speed Passively to Diagnose Diseases”
Can you buy a working radar module for $12? As it turns out, you can. But can you make it output useful information? According to [Mathieu], the answer is also yes, but only if you ignore the datasheet circuit and build this amplification circuit for your dirt cheap Doppler module.
The module in question is a CDM324 24-GHz board that’s currently listing for $12 on Amazon. It’s the K-band cousin of the X-band HB100 used by [Mathieu] in a project we covered a few years back, but thanks to the shorter wavelength the module is much smaller — just an inch square. [Mathieu] discovered that the new module suffered from the same misleading amplifier circuit in the datasheet. After making some adjustments, a two-stage amp was designed and executed on a board that piggybacks on the module with a 3D-printed bracket.
Frequency output is proportional to the velocity of the detected object; the maximum speed for the sensor is only 14.5 mph (22.7 km/h), so don’t expect to be tracking anything too fast. Nevertheless, this could be a handy sensor, and it’s definitely a solid lesson in design. Still, if your tastes run more toward using this module on the 1.25-cm ham band, have a look at this HB100-based 3-cm band radio.
Continue reading “The Right Circuit Turns Doppler Module into a Sensor”
It is pretty easy to go to a big box store and get a digital speedometer for your bike. Not only is that no fun, but the little digital display isn’t going to win you any hacker cred. [AlexGyver] has the answer. Using an Arduino and a servo he built a classic needle speedometer for his bike. It also has a digital display and uses a hall effect sensor to pick up the wheel speed. You can see a video of the project below.
[Alex] talks about the geometry involved, in case your high school math is well into your rear view mirror. The circumference of the wheel is the distance you’ll travel in one revolution. If you know the distance and you know the time, you know the speed and the rest is just conversions to get a numerical speed into an angle on the servo motor. The code is out on GitHub.
Continue reading “Arduino + Geometry + Bicycle = Speedometer”
Why should cyclists have all of the fancy toys? Bicycle computers are very common these days but you won’t find similar hardware for skateboards and longboards. [KobraX22] isn’t taking it lying down. He built this speed and distance computer for his longboard. It doesn’t use very many components and should be easy to install.
The device monitors the rotation of one of the wheels by mounting a reflectance sensor on one of the trucks. It points toward the inside of a wheel which has a piece of black tape on it. Every time the tape passes it prevents the IR led from reflecting back at its paired receiver. This lets the Arduino count the revolutions, which are then paired with the wheel diameter to calculate speed as well as distance traveled. Of course the wheels wear down over time to so frequent riders will have to take new measurements at regular intervals.
[KobraX22] went with a QRB1114 sensor. It costs less than $2 and doesn’t require him to embed a magnet in the wheel like a hall effect sensor setup would have. It also shouldn’t interfere with any other fancy wheel hacks you’ve done, like adding a POV display.
[John] is keeping the neighborhood safe by keeping an eye out for speeders. Well, he’s really keeping a webcam out for speeders. His technique doesn’t use radar or lasers. He’s processing webcam frames in Python to calculate speed.
It comes down to some basic image manipulation. He firsts gathers the images necessary to make the calculations by using a motion-detecting webcam program called YawCam. The images are analyzed to establish which parts have changed between frames; this gets rid of all the stationary objects. Now the frames can be compared to establish the distance in pixels. By calibrating the shot through measurements of the target area, this data can be directly converted into actual distance. It is then compared with the timestamps from each frame to arrive at speed. This can be used for vehicles on the street like we see above, or more whimsical measurements like pet turtle progress.
[Jaroslav’s] camera didn’t have a feature to measure the speed of its response in different modes so he figured out his own method. Using the microphone on his webcam he recorded the sound made by the mirror and shutter movements, then used Audacity to analyze the camera’s performance.
When you get right down to it, this is a fantastic idea. Audacity, the open source audio editing suite, has the ability to show each captured audio track next to each other. That makes it easy for you to precisely align the clips, and has in-build time measuring features with fantastic resolution.
He tested a whole bunch of different settings on a Canon EOS600D DSLR camera. In the image above you can see him comparing performance between different ISO settings. He also looks into different brands and sizes of SD storage cards, as well as the time difference when storing raw image data versus JPEG encoded data.