SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
Accurate real-time estimation of human movement dynamics, including internal
joint moments and muscle forces, is essential for applications in clinical
diagnostics and sports performance monitoring. Inertial measurement units
(IMUs) provide a minimally intrusive solution for capturing motion data,
particularly when used in sparse sensor configurations. However, current
real-time methods rely on supervised learning, where a ground truth dataset
needs to be measured with laboratory measurement systems, such as optical
motion capture. These systems are known to introduce measurement and processing
errors and often fail to generalize to real-world or previously unseen
movements, necessitating new data collection efforts that are time-consuming
and impractical. To overcome these limitations, we propose SSPINNpose, a
self-supervised, physics-informed neural network that estimates joint
kinematics and kinetics directly from IMU data, without requiring ground truth
labels for training. We run the network output through a physics model of the
human body to optimize physical plausibility and generate virtual measurement
data. Using this virtual sensor data, the network is trained directly on the
measured sensor data instead of a ground truth. When compared to optical motion
capture, SSPINNpose is able to accurately estimate joint angles and joint
moments at an RMSD of 8.7 deg and 4.9 BWBH%, respectively, for walking and
running at speeds up to 4.9 m/s at a latency of 3.5 ms. Furthermore, the
framework demonstrates robustness across sparse sensor configurations and can
infer the anatomical locations of the sensors. These results underscore the
potential of SSPINNpose as a scalable and adaptable solution for real-time
biomechanical analysis in both laboratory and field environments.
