SolePoser: Full Body Pose Estimation using a Single Pair of Insole Sensor

We propose SolePoser, a real-time 3D pose estimation system that leverages only a single pair of insole sensors. Unlike conventional methods relying on fixed cameras or bulky wearable sensors, our approach offers minimal and natural setup requirements. The proposed system utilizes pressure and IMU sensors embedded in insoles to capture the body weight’s pressure distribution at the feet and its 6 DoF acceleration. This information is used to estimate the 3D full-body joint position by a two-stream transformer network. A novel double-cycle consistency loss and a cross-attention module are further introduced to learn the relationship between 3D foot positions and their pressure distributions. We also introduced two different datasets of sports and daily exercises, offering 908k frames across eight different activities. Our experiments show that our method’s performance is on par with top-performing approaches, which utilize more IMUs and even outperform third-person-view camera-based methods in certain scenarios.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676418
Citations
5
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
1.25
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article

SolePoser: Full Body Pose Estimation using a Single Pair of Insole Sensor

Rawal Khirodkar, Kris Kitani, Hideki Koike, Erwin Wu
5 citations
Human Pose and Action Recognition
1.25
article

SolePoser: Full Body Pose Estimation using a Single Pair of Insole Sensor

Rawal Khirodkar, Kris Kitani, Hideki Koike, Erwin Wu
article en
5 citations

Abstract

We propose SolePoser, a real-time 3D pose estimation system that leverages only a single pair of insole sensors. Unlike conventional methods relying on fixed cameras or bulky wearable sensors, our approach offers minimal and natural setup requirements. The proposed system utilizes pressure and IMU sensors embedded in insoles to capture the body weight’s pressure distribution at the feet and its 6 DoF acceleration. This information is used to estimate the 3D full-body joint position by a two-stream transformer network. A novel double-cycle consistency loss and a cross-attention module are further introduced to learn the relationship between 3D foot positions and their pressure distributions. We also introduced two different datasets of sports and daily exercises, offering 908k frames across eight different activities. Our experiments show that our method’s performance is on par with top-performing approaches, which utilize more IMUs and even outperform third-person-view camera-based methods in certain scenarios.

Tokyo Institute of Technology (JP), Carnegie Mellon University (US)
Openalex Percentile: Top 19%
Human Pose and Action Recognition
1.25
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