SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation

TL;DR

SSPINNpose is a self-supervised, physics-informed neural network that estimates human joint kinematics and kinetics from IMU data without needing ground truth labels, enabling accurate real-time biomechanical analysis.

Contribution

It introduces a novel self-supervised learning framework that bypasses the need for labeled datasets by using physics models to generate virtual sensor data for training.

Findings

Achieves RMSD of 8.7 degrees in joint angles and 4.9 BWBH% in joint moments.

Operates at a latency of 3.5 ms for real-time applications.

Robust across sparse sensor configurations and can identify sensor locations.

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.