Simple Sensor Provides Detailed Motion Capture for VR Hands

Consider the complexity of the appendages sitting at the end of your arms. The human hands contain over a quarter of the entire complement of bones in the body, use dozens of muscles both in the hand itself and extending up the forearm, and are capable of almost infinite variance in the movements they can create. They are exquisite machines.

And yet when it comes to virtual reality, most simulations treat the hands like inert blobs. That may be partly due to their complexity; doing motion capture from so many joints can be computationally challenging. But this pressure-sensitive hand motion capture rig aims to change that. The product of an undergraduate project by [Leslie], [Hunter], and [Matthew], the idea was to provide an economical and effective way to capture gestures for virtual reality simulators, which generally focus on capturing large motions from the whole body.

The sensor consists of a sandwich of polyurethane foam with strain gauge sensors embedded within. The user slips his or her hand into the foam and rests the fingers on the sensors. A Teensy and twenty lines of code translate finger motions within the sandwich into five axes of joystick movement, which is then sent to Unreal Engine, where finger motions were translated to a 3D-model of a hand to play a VR game of “Rock, Paper, Scissors.”

[Leslie] and her colleagues have a way to go on this; testers complained that the flat hand posture was unnatural, and that the foam heated things up quickly. Maybe something more along the lines of these gesture-capturing gloves would work?

Quartet of SMD Resistors Used to Sense Z-Axis Height

Here’s a neat trick for your next 3D-printer build or retrofit: a Z-axis sensor using a DIY strain gauge made from SMD resistors. We’re betting it could have plenty of other applications, too.

Conventional load cells, at least the ones you can pick up cheaply from the usual sources or harvest from old kitchen or bathroom scales, are usually way too big to be used on the extruder of a 3D-printer. [IvDm] wanted to build a touch sensor for his Hybercube printer, so he built his own load cell to do it. It consists of four 1000 ohm SMD resistors in the big 2512 device size. He mounted them to an X-shaped PCB and wired them in the classic Wheatstone bridge configuration, with two resistors on one side of the board and two on the other.

The extruder mounts into a hole in the center of the board and floats on it. Through an HX711 load cell driver chip, the bridge senses the slight flex of the board when the extruder bottoms out on the bed, and an ATtiny85 pulls a limit switch input to ground. [IvDm] even did some repeatability testing with this sensor and it turned out to be surprisingly consistent. The first minute or so of the video below shows it in action on the Hypercube.

We found the use of SMD resistors as strain gauges pretty clever here, but there’s plenty to do with off-the-shelf load cells: measuring how much filament is left on a roll, checking the thrust of a model rocket engine, or even figuring out if you’re peeing correctly.

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Brushless Motor Thrust Stand Provides Useful Data

When designing model aircraft of any shape or size, it’s useful to know the performance you can expect from the components chosen. For motors and propellers, this can be difficult. It’s always best to test them in combination. However, with the numbers of propeller and motor combinations possible, such data can be tough to come by. [Nikus] decided it would be easier to just do the testing in-house, and built a rig to do so.

The key component in this build is the strain gauge, which comes already laced up with an Arduino-compatible analog-digital converter module. Sourced for under $10 from Banggood, we can’t help but think that we’ve got it easy in 2018. A sturdy frame secures motor and propeller combination to the strain gauge assembly. An ATMEGA328 handles sending commands to the motor controller, reading the strain gauge results, and spitting out data to the LCD.

It’s a cheap and effective build that solves a tricky problem and would be a useful addition to the workshop for any serious modeler. We’ve seen other approaches in this area too, for those eager to graph their motor performance data. Video after the break.

[Thanks to Baldpower for the tip!]

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Kinetic Sculpture Achieves Balance Through Machine Learning

We all know how important it is to achieve balance in life, or at least so the self-help industry tells us. How exactly to achieve balance is generally left as an exercise to the individual, however, with varying results. But what about our machines? Will there come a day when artificial intelligences and their robotic bodies become so stressed that they too will search for an elusive and ill-defined sense of balance?

We kid, but only a little; who knows what the future field of machine psychology will discover? Until then, this kinetic sculpture that achieves literal balance might hold lessons for human and machine alike. Dubbed In Medio Stat Virtus, or “In the middle stands virtue,” [Astrid Kraniger]’s kinetic sculpture explores how a simple system can find a stable equilibrium with machine learning. The task seems easy: keep a ball centered on a track suspended by two cables. The length of the cables is varied by stepper motors, while the position of the ball is detected by the difference in weight between the two cables using load cells scavenged from luggage scales. The motors raise and lower each side to even out the forces on each, eventually achieving balance.

The twist here is that rather than a simple PID loop or another control algorithm, [Astrid] chose to apply machine learning to the problem using the Q-Behave library. The system detects when the difference between the two weights is decreasing and “rewards” the algorithm so that it learns what is required of it. The result is a system that gently settles into equilibrium. Check out the video below; it’s strangely soothing.

We’ve seen self-balancing systems before, from ball-balancing Stewart platforms to Segway-like two-wheel balancers. One wonders if machine learning could be applied to these systems as well.

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Assess Your Output with a Cheap DIY Urine Flowmeter

Some things about the human body are trivial to measure. Height, weight, blood pressure, pulse, temperature — these are all easily quantifiable with the simplest of instruments and can provide valuable insights into our state of health. Electrical activity in the heart and the brain can be captured with more complex instruments, too, and all manner of scopes can be inserted into various orifices to obtain actionable information about what’s going on.

But what about, err, going? Urine flow can be an important leading indicator for a host of diseases and conditions, but it generally relies on subjective reports from the patient. Is there a way to objectively measure how well urine is flowing? Of course there is.

The goal for [GreenEyedExplorer]’s simple uroflowmeter is simple: provide a cheap, easy to use instrument that any patient can use to quantify the rate of urine flow while voiding. Now, we know what you’re thinking — isn’t liquid flow usually measured in a closed system with a paddlewheel or something extending into the stream? Wouldn’t such a device for urine flow either be invasive or messy, or both? Rest assured, this technique is simple and tidy. A small load cell is attached to an ESP8266 through an HX711 load cell amp. A small pan on the load cell receives urine while voiding, and the force of the urine striking the pan is assessed by the software. Reports can be printed to share with your doctor, and records are kept to see how flow changes over time.

All kidding aside, this could be an important diagnostic tool, and at 10€ to build, it empowers anyone to take charge of their health. And since [GreenEyedExplorer] is actually a urologist, we’re taking this one seriously.

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Instrument Packed Pedal Keeps Track of Cyclist’s Power

Exactly how much work is required to pedal a bike? There are plenty of ways to measure the power generated by a cyclist, but a lot of them such as heavily instrumented bottom brackets and crank arms, can be far too expensive for casual use. But for $30 in parts you can build this power-measuring bike pedal. and find out just how hard you’re stoking.

Of course it’s not just the parts but knowing what to do with them, and [rabbitcreek] has put a lot of thought and engineering into this power pedal. The main business of measuring the force applied to the crank falls to a pair of micro load cells connected in parallel. A Wemos, an HX711 load-cell amp, a small LiPo pack and charging module, a Qi wireless charger, a Hall sensor, a ruggedized power switch, and some Neopixels round out the BOM. Everything is carefully stuffed into very little space in a modified mountain bike pedal and potted in epoxy for all-weather use. The Hall sensor keeps tracks of the RPMs while the strain gauges measure the force applied to the pedal, and the numbers from a ride can be downloaded later.

We recall a similar effort using a crank studded with strain gauges. But this one is impressive because everything fits in a tidy package. And the diamond plate is a nice touch.

Load Cells Tell You to Lay Off the Donuts

Our old algebra teacher used to say, “You have to take what you know and use it to get what you don’t know.” That saying always reminds of us sensors that convert physical quantities into things our microcontrollers can measure. Sometimes the key to a project is knowing what kind of sensor will read the physical properties of the system you are interested in. If that physical property is weight, you can use what is known as a load cell. [DegrawSt] uses four 50 kg load cells to create a bathroom scale using an Arduino.

Load cells typically contain strain gauges that change resistance when deformed. This actually measures force, but if you mount them so they measure the force exerted by you standing on a platform, you get a scale. A load cell usually has four strain gauges in a bridge configuration. This causes a voltage across the bridge, although the output can be noisy and on the order of millivolts.

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