Earbuds Orientation Alignment Based on Markov Chain Monte Carlo Sampling
Xianghao Zhan, Nafiul Rashid, Ebrahim Nemati, Mohsin Y. Ahmed, Jilong Kuang
Abstract
Earbuds are instrumental in health monitoring but the orientation can variate among users, which may significantly impact the health-monitoring system generalizability. To study the effect of earbuds orientation heterogeneity and align kinematics across earbuds orientations, we collected a dataset with various rotations relative to a baseline orientation. We developed the coordinate transformation by estimating Euler angles in transformation matrices with either grid search or Markov Chain Monte Carlo (MCMC) sampling. Taking ~ 17 seconds with a personal laptop, the MCMC method accurately estimated the coordinate transformation matrices to enable the transformed tri-axial linear acceleration to better match the baseline tri-axial linear acceleration with an average relative error of 1.899% (0.186 m/s<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) and a maximum relative error of 2.774% averaged over all test orientations. Using the estimated transformation matrices and Samsung dataset of identification of activities of daily living (ADL), we validated the statistically significant impact of earbuds orientation heterogeneity on ADL identification (p < 0.001), which can cause 14.0% reduction in mean accuracy and 18.7% reduction in mean macro-average F1-score. To sum up, the MCMC method developed can be applied in earbuds kinematics alignment to address orientation heterogeneity and enable better earbuds-based health monitoring.
BibTeX
@inproceedings{icassp2025_earbudsorientati,
title = {Earbuds Orientation Alignment Based on Markov Chain Monte Carlo Sampling},
author = {Xianghao Zhan and Nafiul Rashid and Ebrahim Nemati and Mohsin Y. Ahmed and Jilong Kuang},
booktitle = {ICASSP 2025},
year = {2025}
}