IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

Vimal Mollyn, Riku Arakawa, Mayank Goel, Chris Harrison, Karan Ahuja
4/25/2023
cs.HCcs.CV

No official code found. Best implementation: FIGLAB/IMUPoser (156 stars, last commit 2024-12-18).

Abstract

Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear special suits or sensor arrays to achieve this end. Instead, in this work, we explore the feasibility of estimating body pose using IMUs already in devices that many users own -- namely smartphones, smartwatches, and earbuds. This approach has several challenges, including noisy data from low-cost commodity IMUs, and the fact that the number of instrumentation points on a users body is both sparse and in flux. Our pipeline receives whatever subset of IMU data is available, potentially from just a single device, and produces a best-guess pose. To evaluate our model, we created the IMUPoser Dataset, collected from 10 participants wearing or holding off-the-shelf consumer devices and across a variety of activity contexts. We provide a comprehensive evaluation of our system, benchmarking it on both our own and existing IMU datasets.

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Code Implementations(1)

FIGLAB/IMUPoserCommunity100%

Code for IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

15626Shell, PythonFeb 14, 20231 years agoNOASSERTION

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Cite this paper

@article{mollyn2023imuposer,
  title  = {IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds},
  author = {Vimal Mollyn and Riku Arakawa and Mayank Goel and Chris Harrison and Karan Ahuja},
  year   = {2023},
  eprint = {2304.12518},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.12518v1}
}

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