OrientationNN: a physics-informed lightweight neural network for real-time joint kinematics estimation from IMU data

Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their high cost, complex setup, and limited portability restrict use outside laboratory environments. This study proposes a lightweight, physics-informed neural network for real-time joint kinematics estimation using inertial measurement units (IMUs).

We developed OrientationNN, which integrates orientation-based physical constraints into a compact multi-layer perceptron architecture to ensure biomechanically consistent joint kinematics estimation. The model was evaluated on a publicly available dataset and compared with a physics-based inverse kinematics framework (OpenSense) and conventional learning-based models including MLP, LSTM, CNN, and Transformer.

OrientationNN achieved an average joint angle estimation error below 5° during ambulatory motion and consistently outperformed OpenSense across all kinematic variables. The model required only 4.9 × 10³ FLOPs per frame and 10.8 KB of parameters, demonstrating high computational efficiency suitable for real-time applications.

OrientationNN enables accurate and computationally efficient joint kinematics estimation from IMU data. The results highlight its potential as a cost-effective and scalable solution for wearable biomechanical and motion analysis applications.