IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture
CoRR(2024)
摘要
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.
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