If you like playing Grand Theft Auto, you’re pretty familiar with squeezing the triggers for accelerating and braking while driving around. [David Programa] decided this was too easy, and instead developed a system to allow him to pedal his way around the virtual world.
The device relies on a flywheel-based exercise bike, with six magnets placed on the flywheel that triggers a reed switch six times per rotation. The extra magnets give the system better resolution at slow speeds. A Hall Effect sensor would be a more reliable way to build this to survive in the long term, but the reed switch does work. It’s paired with a debounce circuit to keep the output clean. A Raspberry Pi is pressed into service, running a Python program to read a GPIO pin activated by the reed switch, counting pulses to determine the speed of pedalling.
The trigger control used in the Xbox 360 controller is a potentiometer that creates varying voltages depending on its position, allowing it to act as an analog accelerator input. 0 volts corresponds to no input, while the trigger reads 3.3 volts when fully depressed. The Raspberry Pi emulates this with its PWM output, paired with a low-pass filter to create the relevant voltage to inject into the trigger input on a generic Xbox 360 controller.
While it’s a lot less practical than simply using a regular controller, the pedal controls do allow you to get a great workout while playing Grand Theft Auto. Some of the more intense chase missions should be a great way to get your heart rate up, and that’s got to be a good thing.
Ironically, though, the system only works for cars and motorbikes in game. The bicycles in Grand Theft Auto are controlled by mashing the A button instead. Alternatively, you might consider a similar system for playing Mario Kart on the Nintendo Switch. Video after the break.
We’re currently in the midst of New Year’s Resolutions season, which means an abundance of spanking new treadmills and exercise bikes. And one thing becomes quickly obvious while using those machines: the instruments on them are, at best, only approximately useful for measuring things like your pulse rate, and in the case of estimating the calories burned by your workout, are sometimes wildly optimistic.
If precision quantification of your workout is your goal, you’ll need to monitor your “VO2 max”, a task for which this portable, printable mask is specifically designed. This is [Robert Werner]’s second stab at a design that senses both pressure differential and O2 concentration to calculate the maximum rate of oxygen usage during exercise. This one uses a commercially available respirator, of the kind used for painting or pesticide application, as the foundation for the build. The respirator’s filter elements are removed from the inlets to provide free flow of air into the mask, while a 3D printed venturi tube is fitted to its exhaust port. The tube houses the pressure and O2 sensors, as well as a LiPo battery pack and an ESP32. The microcontroller infers the volume of exhaled air from the pressure difference, measures its O2 content, and calculates the VO2 max, which is sent via Bluetooth to a smartphone running an exercise tracking app like Zwift or Strava.
[Robert] reports that his $100 instrument compares quite well to VO2 max measurements taken with a $10,000 physiology lab setup, which is pretty impressive. The nice thing about the design of this mask is how portable it is, and how you can take your exercise routine out into the world — especially handy if your fancy exercise bike gets bricked.
The first step was to use some apps on his Android phone to reveal the profiles on the bike, which showed his particular machine used a Nordic Bluetooth UART. This meant the only work would be decoding the stream of bytes coming off the wireless serial port. Using Wireshark and Bluetooth logs on his phone, [Wayne] was able to correspond the various commands to points in the video. There were still a few bytes that he wasn’t able to identify, but [Wayne] had enough to whip up a quick .NET app that can start a workout and log it all to a database. The code for his app is on his GitHub.
While [Wayne] doesn’t specifically name the bike he uses in this project, we tracked down the image he shows on his writeup to the Exerpeutic 900e. It appears to be discontinued but the reverse engineering approach should be usable on a range of Bluetooth-connected machines. This isn’t the first bike we’ve seen liberated by reverse engineering here at Hackaday. And we have a feeling it won’t be the last.
Hamsters are great pets, especially for those with limited space or other resources. They are fun playful animals that are fairly easy to keep, and are entertaining to boot. [Kim]’s hamster, [Mr. Fluffbutt], certainly fits this mold as well but [Kim] wanted something a little beyond the confines of the habitat and exercise wheel and decided to send him on a virtual journey every time he goes for a run.
The virtual hamster journey is built on an ESP32 microcontroller which monitors the revolutions of the hamster wheel via a hall effect sensor and magnet. It then extrapolates the distance the hamster has run and sends the data to a Raspberry Pi which hosts a MQTT and Node.js server. From there, it maps out an equivalent route according to a predefined GPX route and updates that information live. The hamster follows the route, in effect, every time it runs on the wheel. [Mr Fluffbutt] has made it from the Netherlands to southeastern Germany so far, well on his way to his ancestral home of Syria.
This project is a great way to add a sort of augmented reality to a pet hamster, in a similar way that we’ve seen self-driving fish tanks. Adding a Google Streetview monitor to the hamster habitat would be an interesting addition as well, but for now we’re satisfied seeing the incredible journey that [Mr Fluffbutt] has been on so far.
Here at Hackaday we’re always exited to see hacks that recycle our favorite childhood consoles into something new and interesting. In that context, it’s not so uncommon to see mods which combine new and unusual control methods with old devices in ways that their manufacturers never intended. What [Mike Choi] has built with the Labo Fit Adventure Kit is the rare hack that combines radically new control schemes with a modern console: without actually modifying any hardware.
In short, the Labo Fit Adventure Kit lets the player play Mario Kart on the Nintendo Switch by riding a stationary exercise bike, steering with a wheel, and squeezing that wheel to use items. The Fit Kit combines the theme of Labo, Nintendo’s excellent cardboard building kit for the Nintendo Switch with the existing Ring-Con accessory for the unrelated Nintendo game Ring Fit Adventure plus a collection of custom hardware to tie it all together. That hardware senses cadence on the stationary bike, watches for the user to squeeze the handheld wheel controller, and translates those inputs to button presses on the controller to play the game.
The most fascinating element of this project is the TAPBO module which adapts the Joy-Con controller to remote input. The module includes electronics, actuators, and a clever mechanical design to allow it to be mounted to the Ring-Con in place of an unmodified Joy-Con. Electrically the components will be familiar to regular Hackaday readers; there is a breakout board for a Teensy which also holds an XBee module to receive inputs remotely and drive a pair of servos. The entire module is described in detail starting at 4:42 in the video.
Mechanically the TAPBO relies on a pair of cam-actuated arms which translate rotational servo motion into linear action to press shoulder or face buttons. The module directly measures flex of the Ring-Con with an added flexible resistor and receives cadence information from another module embedded in the stationary bike via Zigbee. When these inputs exceed set thresholds they drive the servos to press the appropriate controller buttons to accelerate or use an item.
We’ve focused pretty heavily on the technical aspects of this project, but this significantly undersells the level of polish and easy to understand documentation [Mike] has produced. It includes a TAPBO Amiibo in customized packaging, and more. Check out the full video to get the complete scope of this project.
Recently [Imran Haque]’s family bought the quite popular Peloton bike. After his initial skepticism melted to a quiet enthusiasm, [Imran] felt his hacker curiosity begin to probe the head unit on the bike. Which despite being a lightly skinned android tablet, has a reputation for being rather locked down. The Peloton bike will happily collect data such as heart rate from other devices but is rather reticent to broadcast any data it generates such as cadence and power. [Imran] set out to decode and liberate the Peleton’s data by creating a device he has dubbed PeloMon. He credits the inspiration for his journey to another hacker who connected a Raspberry Pi to their bricked exercise bike.
As a first step, [Imran] step began with decoding the TRRS connector that connects the bike to the head unit. With the help of a multi-meter and a logic analyzer, two 19200bps 8N1 RS-232 channels (TX and RX) were identified. Once the basic transport layer was established, he next set to work decoding the packets. By plotting the bytes in the packets and applying deductive reasoning, a rough spec was defined. The head unit requested updates every 100ms and the bike responded with cadence, power, and resistance data depending on the request type (the head unit did a round-robin through the three data types).
Once the protocol was decoded, the next step for [Imran] was to code up an emulator. It seems a strange decision to write an emulator for a device with a simple protocol, but the reasoning is quite sound. It avoids a 20-minute bike ride every time a code change needs to be tested. [Imran] wrote both an event-driven and a timing-accurate emulator. The former runs on the same board as the PeloMon and the latter runs on a separate board (an Arduino).
The hardware chosen for the PeloMon was an Adafruit Feather 32u4 Bluefruit LE. It was chosen for supporting Bluetooth LE as well as having onboard EEPROM. A level shifter allows the microcontroller to talk directly to the RS-323 on the bike. After a few pull requests to the Adafruit Bluetooth libraries and a fair bit of head-banging, [Imran] has code that advertises two Bluetooth services, one for speed and another for power. A Bluetooth serial console is also included for debugging without having to pull the circuit out.
Getting a wheeled robot to follow a line is relatively simple, but a running human is by no means a stable sensor platform. At the previously mentioned hackathon, developers put together a rough proof of concept with a smartphone, using its camera to recognize a painted line on the ground and provide left/right audio cues. As the project developed, the smartphone was attached to a waist belt and bone conduction headphones were used, which don’t affect audio situational awareness as much as normal headphones.
The shaking and side to side movement of running, and varying light conditions and visual obstructions in the outdoors made the problem more difficult to solve, but within a year the developers had completed successful running tests with [Thomas] on a well-lit indoor track and an outdoor pedestrian path with a temporary line. For the first time in 25 years, [Thomas] was able to run independently.
While guide dogs have proven effective for both daily life and running, they cost approximately $60000 over an average working life of 8 years, putting them out of reach of many sight-impaired people around the world. Project Guideline is still in the early stages, and real-world problems like obstacles and traffic still need to be addressed, but there is massive potential.