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. 2024 Dec 20;11(12):1299.
doi: 10.3390/bioengineering11121299.

The Effect of Thigh Muscle Forces on Knee Contact Force in Female Patients with Severe Knee Osteoarthritis

Tingting Liu  1 , Hao Xie  1 , Songhua Yan  1 , Jizhou Zeng  2 , Kuan Zhang  1
Affiliations

The Effect of Thigh Muscle Forces on Knee Contact Force in Female Patients with Severe Knee Osteoarthritis

Tingting Liu et al. Bioengineering (Basel). .

Abstract

Thigh muscles greatly influence knee joint loading, and abnormal loading significantly contributes to the progression of knee osteoarthritis (KOA). Muscle weakness in KOA patients is common, but the specific contribution of each thigh muscle to joint loading is unclear. The gait data from 10 severe female KOA patients and 10 controls were collected, and the maximum isometric forces of the biceps femoris long head (BFL), semitendinosus (ST), rectus femoris (RF), vastus lateralis (VL), and vastus medialis (VM) were calibrated via ultrasound. Four musculoskeletal (MSK) models were developed based on EMG-assisted optimization, static optimization, and ultrasound data. The ultrasound-calibrated EMG-assisted MSK model achieved higher accuracy (R2 > 0.97, RMSE < 0.045 Nm/kg). Patients exhibited increased VL and VM forces (p < 0.004) and decreased RF force (p < 0.006), along with elevated medial and total joint contact forces (p < 0.001) and reduced lateral forces (p < 0.001) compared to controls. The affected side relied on VL and BFL the most (p < 0.042), while RF was key for the unaffected side (p < 0.003). Ultrasound calibration and EMG-assisted optimization significantly enhanced MSK model accuracy. Patients exerted greater quadriceps and hamstring forces bilaterally, shifting knee loading medially, and depended more on the lateral thigh muscles on the affected side. Hamstrings contributed more to joint contact forces, while quadriceps' contributions decreased.

Keywords: knee contact force; knee osteoarthritis; maximum isometric force; muscle force; musculoskeletal model; ultrasound.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
MATLAB was utilized for the initial processing of experimental data, encompassing marker trajectories, ground reaction forces (GRFs), and electromyography (EMG) signals. This processing involved filtering, normalizing EMG signals, and converting the data for compatibility with OpenSim. Utilizing OpenSim’s APIs, we scaled the model and performed inverse kinematic analysis, muscle analysis, and inverse dynamics analysis to ascertain muscle–tendon unit (MTU) lengths, moment arms, and joint moments. Muscle parameters were anthropometrically adjusted and integrated into the CEINMS calibration procedure to enhance parameter accuracy, thereby reducing the difference between experimental and predicted joint moments. Ultrasound data was employed to calibrate the maximum isometric force. By integrating two distinct neural control algorithms with varying levels of muscle parameter calibration, we ultimately developed four unique models. The CEINMS execution module utilized the refined muscle activation data, in conjunction with MTU lengths and moment arms from OpenSim, to estimate MTU forces and joint moments from individual experimental trials.
Figure 2
Figure 2
Comparisons of knee contact force disparity.
Figure 3
Figure 3
Muscle contributions to knee contact forces during the stance phase.
See this image and copyright information in PMC

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