This paper presents a novel approach for predicting human poses using IMU data, diverging from previous studies such as DIP-IMU, IMUPoser, and TransPose, which use up to 6 IMUs in conjunction with bidirectional RNNs. We introduce two main innovations: a data-driven strategy for optimal IMU placement and a transformer-based model architecture for time series analysis. Our findings indicate that our approach not only outperforms traditional 6 IMU-based biRNN models but also that the transformer architecture significantly enhances pose reconstruction from data obtained from 24 IMU locations, with equivalent performance to biRNNs when using only 6 IMUs. The enhanced accuracy provided by our optimally chosen locations, when coupled with the parallelizability and performance of transformers, provides significant improvements to the field of IMU-based pose estimation.

利用IMU数据预测人体姿势的研究表明，采用数据驱动的策略和基于Transformer的模型架构可以在时间序列分析中提供等同于6个IMU的性能，从而显著提高了IMU基础姿态估计领域的精度。

IMUOptimize：一种基于数据驱动的优化IMU放置方法，用于基于Transformer架构的人体姿态估计