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Design Factors Affecting IMU-Based Joint Torque Estimation Using Deep Learning
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DOI：10.1109/JSEN.2026.3688181.png)
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Abstract
En 中文
Accurate lower limb joint torque estimation is important for gait analysis, rehabilitation planning, and the control of assistive devices. While inertial measurement units (IMUs) provide a portable alternative to laboratory-based motion capture systems, the influence of key design choices on deep learning-based joint torque estimation remains insufficiently understood. This study systematically evaluated five factors affecting lower limb joint torque estimation across five degrees of freedom: hip flexion, hip adduction, hip rotation, knee flexion, and ankle dorsiflexion. Using a public dataset of 20 healthy adults during treadmill walking, we examined model architecture, sampling frequency, sliding window size, IMU sensor configuration, and data normalization method. Each configuration was evaluated over ten independent training runs. Recurrent neural networks outperformed the other architectures, with long short-term memory (LSTM) achieving the strongest overall performance. Among the tested settings, 100-Hz sampling, a 64-sample sliding window, a four-sensor configuration, and Z-score normalization gave the best overall results. The resulting benchmark configuration achieved an average Pearson correlation coefficient (R) of 0.97 across five lower limb joint torque estimation tasks. These findings provide an evidence-based reference for IMU-based joint torque estimation and support the development of wearable biomechanics for clinical gait analysis, rehabilitation, and assistive technology.
Keywords:
Gait
inertial measurement unit (IMU)
joint torque estimation
long short-term memory (LSTM)
wearable sensors
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