Most gyms are closed right now due to social distancing rules, which is what we’re using as our latest excuse to justify our sloth-like lifestyle. But apparently some people miss working out enough that they’re putting together impromptu home gyms. [Michael Pick] has even outfitted his DIY pull-up station with an Arduino to keep track of his exercise while on lockdown. You may not like it, but this is what peak performance looks like.
In the video after the break, [Michael] explains the design and construction of the bar itself which technically could be thought of as its own project. Obviously the Arduino counter isn’t strictly necessary, so if you just wanted to know how to put some scraps of wood and suitably beefy rod together in such a way that it won’t rip off the wall when you put your weight on it, this video is for you.
Towards the end of the video, he gets into an explanation of the electronic side of the project. Inside the 3D printed enclosure is an Arduino Pro Mini, a HC-SR04 ultrasonic sensor, and a 1602 serial LCD. Once the gadget has been mounted in the proper position and activated, it will count how many pull-ups [Michael] has done on the screen.
While we historically haven’t seen a whole lot in the way of homebrew exercise equipment, the current COVID-19 situation does seem to be getting the adrenaline flowing for some of you. We recently covered some DIY dumbbells made from hardware store finds that would be an excellent first project for any hackers who’ve recently been ejected from the Matrix and are trying to use their muscles for the first time.
Getting exact statistics on one’s physical activities at the gym, is not an easy feat. While most people these days are familiar with or even regularly use one of those motion-based trackers on their wrist, there’s a big question as to their accuracy. After all, it’s all based on the motions of just one’s wrist, which as we know leads to amusing results in the tracker app when one does things like waving or clapping one’s hands, and cannot track leg exercises at the gym.
To get around the issue of limited sensor data, researchers at Carnegie Mellon University (Pittsburgh, USA) developed a system based around a camera and machine vision algorithms. While other camera solutions that attempt this suffer from occlusion while trying to track individual people as accurately as possible, this new system instead doesn’t try to track people’s joints, but merely motion at specific exercise machines by looking for repetitive motion in the scene.
The basic concept is that repetitive motion usually indicates forms of exercise, and that no two people at the same type of machine will ever be fully in sync with their motions, so that merely a handful of pixels suffice to track motion at that machine by a single person. This also negates many privacy issues, as the resolution doesn’t have to be high enough to see faces or track joints with any degree of accuracy.
In experiments at the university’s gym, the accuracy of their system over 5 days and 42 hours of video. Detecting exercise activities in the scene was with a 99.6% accuracy, disambiguating between simultaneous activities was 84.6% accurate, while recognizing exercise types was 93.6% accurate. Ultimately repetition counts for specific exercises were within 1.7 counts.
Maybe an extended version of this would be a flying drone capturing one’s outside activities, giving one finally that 100% accurate exercise account while jogging?