# Towards Self-Calibrating Inertial Body Motion Capture

arXiv: https://arxiv.org/abs/1606.03754 · HTML: https://mlsanity.com/paper/1606.03754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture](https://mlsanity.com/paper/2506.10580/) (2025)
- [Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies](https://mlsanity.com/paper/2205.10778/) (2022)

## Abstract (arXiv)

This paper presents a novel online capable method for simultaneous estimation of human motion in terms of segment orientations and positions along with sensor-to-segment calibration parameters from inertial sensors attached to the body. In order to solve this ill-posed estimation problem, state-of-the-art motion, measurement and biomechanical models are combined with new stochastic equations and priors. These are based on the kinematics of multi-body systems, anatomical and body shape information, as well as, parameter properties for regularisation. This leads to a constrained weighted least squares problem that is solved in a sliding window fashion. Magnetometer information is currently only used for initialisation, while the estimation itself works without magnetometers. The method was tested on simulated, as well as, on real data, captured from a lower body configuration.