Accuracy-Sensing Smart Sports Rebounder with ESP32
Updated: Apr 16
Foreward
I created a smart sports rebounder using 4 x acceleration sensors (accelerometers) with an ESP32 microcontroller and machine learning (neural network) to calculate and log a soccer ball's bounce position. This article touches on different aspects of mechanical, electrical, and software engineering, which, when utilized together, can create powerful results! I hope you find this open source project educational and informative.
[Demonstration video link is forthcoming.]
Article sections
Introduction
Supplies
Mechanical Hardware
Electrical Hardware
Microcontroller Programming
Machine Learning
Structure
Training
Result
Real-Time Operation
Conclusion
Introduction
Most sports projects and commercial products utilize a vision system when they want to track the position of a ball, for example in this project by Youtuber Stuff Made Here, where he created a moving basketball hoop that doesn't let you miss a shot.
Such systems are inspiring and impressive, but most require a vision system to track the ball's flight path. If the camera lens gets wet, dirty, or obstructed, or if the lighting is insufficient, this can affect the tracking results. I wanted to build a sports training tool that (1) doesn't require a vision system, (2) can be used at night, and (3) can be implemented with a low-cost microcontroller. Sports rebounders are perfect candidates for such a tool, because the meaningful training data has to do with the impact of the ball, not its flight through the air.
Rebounders are simple and self-explanatory - you throw or kick a ball at a springy backboard, and the ball is rebounded back to the player. The vast majority of rebounders on the market today don't provide measurements or feedback to the player. The closest thing I've found to a non-vision based bounce tracking system is this commercial trampoline system, which uses a proprietary algorithm/calculation to identify bounces.
The idea I wanted to try for myself is to mount 4 x accelerometers on a rebounder net. These types of sensors output the acceleration along 3 x axes, as well as the rate of rotation about those 3 x axes. I could have tried collecting the acceleration data and then teasing the ball landing position out from it using a closed-form equation or algorithm, but given their rise in accessibility in the last few years, I realized that machine learning would be perfect for this application.
Neural networks are one such type of machine learning - they are based our understanding of how neurons in the human brain are believed to operate, and they excel at modeling relationships between input (the accelerometer data) and output (any information we want to learn about the ball impact.) In the end, this machine learning structure was able to infer the soccer ball landing position with surprising accuracy!
Disclosure: Some of the links in this article are affiliate links. This means that, at zero cost to you, I will earn an affiliate commission if you click through the link and finalize a purchase.
Supplies
1 x 3-foot piece of 1/2" EMT conduit
Roughly 18 feet of 3/4" EMT conduit
Various bolts (1/4"-20 thread, #2 self-tapping, etc.)
1 x 18 thickness black Nylon net with rope border, 15 rows x 15 columns with 1-1/4" squares
2 x Yards of 210 Denier black Nylon coated fabric
1 x USB-C cable
1 x ESP32-based custom PCB (bill of materials included below.)
1 x Hammond black ABS enclosure for the PCB, part number 1593LBK
2 x 5mm LED holder
Laptop or PC to run Python scripts
1 x USB Webcam
(Optional) 1 x Voxelab Aries FDM 3D printer (or equivalent FDM 3D printer)
(Optional) - Brother SE600 Sewing and Embroidary machine (or equivalent sewing machine)
Mechanical Hardware
I created the rebounder frame out of cheap and readily-available metal tubing called EMT (electrical metallic tubing) conduit, which is typically used by electricians for construction wiring, but has grown increasingly popular in recent years for home projects. I cut the tubes to length and removed their sharp edge (deburred them) using a rotary deburring tool for the inner diameter (ID) and a hand file on the outer diameter (OD.) I used a conduit hand bender tool to make the corners. These 3/4" conduit pieces were connected end-to-end using metal set screw couplings.
On the rear of the frame, I used 4 x U-shaped metal hinges plus shoulder bolts to allow the frame tilt angle to be set. A center pole made from 1/2" conduit and 3/4" conduit utilizes an EMT conduit telescoping coupling to lock the tilt angle. This coupling is available in my online store, with free shipping available for all orders in the USA. Shipping to Canada is also available. To ensure all pieces would fit together, I modeled the PCB, enclosure, and EMT conduit telescoping pole using Autodesk Fusion 360. The CAD model render is below, along with the actual assembly.
I drilled holes in the sides of the front frame using a cheap drill press, installed rivet nuts (rivnuts) using this tool, and then threaded metal #8-32 thread, 1-7/8" J-bolt into them, locking the position with an extra nut. These hooks are used to hold the elastic shock cord (bungee cord) which loops through the outer edge of the net.