Long Short-Term Memory Network for Contralateral Knee Angle Estimation During Level-Ground Walking: A Feasibility Study on Able-Bodied Subjects

Abstract

Recent reports have revealed that the number of lower limb amputees worldwide has increased as a result of war, accidents, and vascular diseases and that transfemoral amputation accounts for 39% of cases, highlighting the need to develop an improved functional prosthetic knee joint that improves the amputee's ability to resume activities of daily living. To enable transfemoral prosthesis users to walk on level ground, accurate prediction of the intended knee joint angle is critical for transfemoral prosthesis control. Therefore, the purpose of this research was to develop a technique for estimating knee joint angle utilizing a long short-term memory (LSTM) network and kinematic data collected from inertial measurement units (IMUs). The proposed LSTM network was trained and tested to estimate the contralateral knee angle using data collected from twenty able-bodied subjects using a lab-developed sensory gadget, which included four IMUs. Accordingly, the present work represents a feasibility investigation conducted on able-bodied individuals rather than a clinical validation for amputee gait. This study contributes to the field of bionics by mimicking the natural biomechanical behavior of the human knee joint during gait cycle to improve the control of artificial prosthetic knees. The proposed LSTM model learns the contralateral knee's motion patterns in able-bodied gait and demonstrates the potential for future application in prosthesis control, although direct generalization to amputee users is outside the scope of this preliminary study. The contralateral LSTM models exhibited a real-time RMSE range of 2.48-2.78° and a correlation coefficient range of 0.9937-0.9991. This study proves the effectiveness of LSTM networks in estimating contralateral knee joint angles and shows their real-time performance and robustness, supporting its feasibility while acknowledging that further testing with amputee participants is required.

Keywords: IMUs; LSTM; contralateral; knee joint angle; sensory gadget.

Figure 1

The flowchart of the present research.

Figure 2

Sensor locations and orientation during level-ground walking; the figure is adapted from [13].

Figure 3

Sensor device attached while performing level-ground walking: (a) side view, (b) front view.

Figure 4

Standard structure of an LSTM cell [23].

Figure 5

Learning curves of (a) RL LSTM network and (b) LR LSTM network.

Figure 6

Comparison of evaluation parameters for two contralateral networks.

Figure 7

Comparison of actual angles and estimated angles obtained using contralateral networks for segment of validation data.

Figure 8

Comparison of real-time evaluation parameters with validation parameters for RL and LR LSTM networks.

Figure 9

Real-time test results for (a) LR LSTM network and (b) RL LSTM network.