What question did this study set out to answer?

This work aims to accurately estimate lower-extremity multi-joint kinematics using a single inertial measurement unit sensor.

July 11, 2026Cognitive ComputationOpen Access

Estimating Lower-Extremity Multi-Joint Kinematics with One IMU Sensor via Attention-based Temporal Convolutional Neural Network

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Randomized trial estimates multi-joint kinematics in lower extremities, suggesting a low-burden solution for clinical settings.

This work aims to accurately estimate lower-extremity multi-joint kinematics using a single inertial measurement unit sensor.
Developed a deep learning model combining a modified Informer encoder with a temporal convolutional neural network.
Compared performance against baseline models like gated recurrent unit and long short-term memory across three public datasets.
Used metrics such as root mean square error and Pearson correlation coefficient for evaluation.
The proposed model outperformed baseline methods in estimating lower-extremity joint angles in most cases.
Demonstrated effective extraction of temporal signal characteristics and long-term dependencies.
The method reduces sensor complexity while maintaining accuracy in multi-joint kinematics estimation.

Wang et al. (2026) studied this question.

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