IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture
<|reference_start|>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 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.<|reference_end|>
@article{ramani2024imuoptimize:,
    title={IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human
  Pose Estimation with Transformer Architecture},
    author={Varun Ramani and Hossein Khayami and Yang Bai and Nakul Garg and
  Nirupam Roy},
    journal={arXiv preprint arXiv:2402.08923},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08923},
    primaryClass={cs.LG}
}