Muscle dynamics analysis by clustered categories during jogging in patients with anterior cruciate ligament deficiency

Abstract

Abstract Background Patients with anterior cruciate ligament (ACL) deficiency (ACLD) tend to have altered lower extremity dynamics. Little is known about the changes in dynamic function and activation during jogging in patients with ACLD. Methods Twenty patients with an injured ACL before ACL reconstruction (ACLD group) and nine healthy male volunteers (control group) were recruited. Each volunteer repeated the jogging experiment five times. Based on the experimental data measured, a musculoskeletal multibody dynamics model was employed to simulate the tibiofemoral joint dynamics during jogging. Eighteen muscles were used for analysis. The obtained dynamics data were used for clustering and curve difference analysis. Results The 18 muscles studied were divided into 3 categories. All the quadriceps, the soleus, the gastrocnemius, and the popliteus were classified as label 1. All the hamstrings were classified as label 2, and the sartorius muscles were classified as label 3. Among them, the classification of the short head of the biceps femoris was significantly different between the two groups (P < 0.001). The force curves of all 18 muscles and the between-group differences were studied according to clustered categories. Most muscle force in label 1 was approaching zero in the terminal stance phase, which was significantly lower than that in the control group (P < 0.05). The muscle force in label 2 had areas with significant differences in the stance phase. Muscle force in label 3 was significantly lower than that in the control group in the pre-swing phase. Conclusions This study showed that there are various changes of muscle function and activation in patients with ACLD. Through clustering and curve analysis, the joint reactions and changes of different muscle forces in the gait cycle between the ACLD and control groups could be further clarified.

Keywords

• Anterior cruciate ligament deficiency
• Clustering
• Dynamics
• Muscle force
• Gait analysis