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This is a Quantitative Study study.

Continuous prediction of lower limb joint parameters for robotic rehabilitation based on horizontal position of centre of mass

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This research demonstrates a new method for predicting joint parameters in rehabilitation robots, reducing sensor usage while maintaining accuracy.

Joint parameters can be predicted continuously using the horizontal position of the center of mass in rehabilitation robots.
The proposed method reduces the number of sensors needed by 50% while maintaining prediction accuracy comparable to IMU or sEMG.
Three models, including Autoencoding-LSTM and CNN-LSTM, are compared to find the best fit for predicting ankle, knee, and hip joint parameters.
Different models show varying effectiveness in predicting joint parameters for different limbs, indicating tailored applications may be needed.

Jin-jian et al. (2025) studied this question.

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This research demonstrates a new method for predicting joint parameters in rehabilitation robots, reducing sensor usage while maintaining accuracy.