Background: Anterior Cruciate Ligament (ACL) is one of the stabilizing ligaments present in the knee joint. An ACL rupture is the most common knee injury, dramatically altering the biomechanics of the knee joint. Most important decreasing knee stability. The aim of this study was on recovering healthy knee motion in an ACL deficient knee during daily activities by developing a control system that controls pneumatic artificial muscles (PAMs) on a soft robotic knee brace.
Method: A control system was developed which utilizes iterative learning by exploiting the iterative behavior of human gait with the focus on reducing the laxity in the tibiofemoral anterior-posterior translation and internal-external rotation. To validate the proposed control system, it was deployed on a virtual ACL deficient knee model with a soft robotic brace.
Results: Our results show that validation on a virtual ACL deficient knee model with a soft robotic brace yielded a maximum recovery of 75.5% in the anterior-posterior translational direction and 36.8% in the internal-external rotational direction after learning 12 seconds during human gait. It further showed that the proposed control system was able to prioritize anterior-posterior translation or internal-external rotation in case of actuator saturation.
Conclusion: The proposed control system is able to partially recover healthy knee motion in an ACL deficient knee during human gait by reducing the laxity caused by ACL lesions.
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