# Intelligent Sports Weights

## TL;DR

This paper introduces a low-cost, real-time embedded system that monitors weightlifting form to prevent injuries and improve performance.

## Contribution

A novel embedded system using a CNN and IMU data for real-time weightlifting supervision with high accuracy.

## Key findings

- The system achieves real-time monitoring with an average accuracy of nearly 95%.
- Prototypes were validated in an operational environment and enclosed in a custom 3D case.
- All research outputs and engineering models are publicly available for use and adaptation.

## Abstract

Weightlifting is a common fitness activity and can be practiced individually without supervision. However, performing regular weightlifting exercises without any form of feedback can lead to serious injuries. To counter this, this work proposes a different approach to automatic weightlifting supervision off-the-person. The proposed embedded system is coupled to the weights and evaluates if they follow the correct trajectory in real time. The system is based on a low-power embedded System-on-a-Chip to perform the classification of the correctness of physical exercises using a Convolutional Neural Network with data from the embedded IMU. It is a low-cost solution and can be adapted to the characteristics of specific exercises to fine-tune the performance of the athlete. Experimental results show real-time monitoring capability with an average accuracy close to 95%. To favor its use, the prototypes have been enclosed on a custom 3D case and validated in an operational environment. All research outputs, developments, and engineering models are publicly available.