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Updated: Feb 28, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

Long Short-Term Memory Network for Contralateral Knee Angle Estimation During Level-Ground Walking: A Feasibility

Ala'a Al-Rashdan¹, Hala Amari¹, Yahia Al-Smadi^2,3

¹Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan.

|February 27, 2026

Summary

Researchers developed a long short-term memory (LSTM) network to predict knee joint angles using inertial measurement units (IMUs). This technique shows promise for improving prosthetic knee control in transfemoral amputees.

Keywords:

IMUs LSTM contralateral knee joint angle sensory gadget

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Last Updated: Feb 28, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

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Bilateral Assessment of the Corticospinal Pathways of the Ankle Muscles Using Navigated Transcranial Magnetic Stimulation

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Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

Area of Science:

Bionics and Biomedical Engineering
Rehabilitation Engineering
Artificial Intelligence in Healthcare

Background:

Rising incidence of lower limb amputations due to war, accidents, and vascular diseases, with transfemoral amputations comprising 39% of cases.
Critical need for advanced prosthetic knee joints to enhance functional mobility and daily living activities for amputees.
Accurate prediction of knee joint angle is essential for effective transfemoral prosthesis control during gait.

Purpose of the Study:

To develop and evaluate a novel technique for estimating knee joint angles using Long Short-Term Memory (LSTM) networks.
To utilize kinematic data from Inertial Measurement Units (IMUs) for real-time knee angle prediction.
To investigate the feasibility of LSTM networks in mimicking natural knee biomechanics for prosthetic applications.

Main Methods:

Training and testing an LSTM network with kinematic data from twenty able-bodied subjects.
Utilizing a custom sensory gadget equipped with four IMUs to collect data.
Estimating the contralateral knee angle as a proxy for intended prosthetic knee movement.

Main Results:

The LSTM models demonstrated high accuracy in estimating contralateral knee joint angles, with a real-time Root Mean Square Error (RMSE) range of 2.48-2.78°.
A high correlation coefficient range of 0.9937-0.9991 was achieved, indicating robust performance.
The study confirmed the real-time performance and robustness of LSTM networks for knee angle estimation in able-bodied individuals.

Conclusions:

LSTM networks are effective for estimating contralateral knee joint angles, showing potential for prosthetic knee control.
The developed technique demonstrates feasibility and robustness, paving the way for future research in bionic limb development.
Further clinical validation with amputee participants is necessary to generalize findings and optimize prosthesis control.