IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture
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 in- troduce two main innovations: a data-driven strategy for optimal IMU placement and a transformer-based model ar- chitecture for time series analysis. Our findings indicate that our approach not only outperforms traditional 6 IMU- based biRNN models but also that the transformer architec- ture significantly enhances pose reconstruction from data ob- tained 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.