Duarte Olga Dos Santos, Jacinto Gustavo, Véstias Mário, Policarpo Duarte Rui
Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emidio Navarro, 1, 1959-007 Lisboa, Portugal.
INESC INOV, 1000-029 Lisboa, Portugal.
Sensors (Basel). 2025 Jun 18;25(12):3808. doi: 10.3390/s25123808.
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.
举重是一项常见的健身活动，可以在无人监督的情况下单独进行。然而，在没有任何形式反馈的情况下进行常规举重练习可能会导致严重受伤。为了解决这个问题，这项工作提出了一种不同的方法来对离人自动举重进行监督。所提出的嵌入式系统与杠铃相连，并实时评估它们是否遵循正确的轨迹。该系统基于一个低功耗嵌入式片上系统，利用来自嵌入式惯性测量单元（IMU）的数据，通过卷积神经网络对体育锻炼的正确性进行分类。这是一种低成本的解决方案，可以根据特定练习的特点进行调整，以微调运动员的表现。实验结果表明，该系统具有实时监测能力，平均准确率接近95%。为了便于使用，原型已被封装在一个定制的3D外壳中，并在实际操作环境中进行了验证。所有的研究成果、开发内容和工程模型都是公开可用的。
