Human motion analysis plays an important role in understanding normal function as well as pathological abnormalities of human musculoskeletal systems. Among different motion analysis techniques, skin marker-based stereophotogrammetry is the one being most widely used in biomechanical community due to its many advantages. A major limitation of this technique is that motion-tracking markers are attached to skin surface of body segment and these markers can move relative to the underlying bone during activities. The relative movement between skin markers and the underlying bones is usually referred to as soft tissue artifact (STA) and it has been proved to be a major source of error of the technique. Much effort has been devoted by the research community to developing techniques to compensate for STA effects and improve motion analysis accuracy. However, the problem has not yet been solved satisfactorily.

In the framework of this dissertation, a new STA compensation method was developed based on in vivo soft tissue movements and inter-subject similarities. First, it was demonstrated that soft tissue movement on lower extremity has inter-subject similarities, which was a new insight contrary to the prevailing opinion. Second, a simultaneous fluoroscopy and stereophotogrammetry motion study was conducted to assess STA in vivo on six subjects who had total knee arthroplasty (TKA), during a series of knee flexion/extension movements and a step-up activity. Both inter-subject similarity and inter-motor-task similarity were observed on the STA results. Third, a universal STA model was constructed using multilinear regression on the STA measurements obtained from multiple subjects and multiple activities. Based on the universal STA model, a new STA compensation concept was implemented in two methods: a STA deduction (STAD) method and a directional weighted optimization (DWO) method. The performance of the two methods was evaluated on in vivo knee joint kinematics. Both methods demonstrated improved performance over conventional rigid body optimization (RBO) method, and the STAD method exhibited the best performance. Overall, the STAD method reduced analysis errors by 36% to 76% for different kinematic components. Finally, the newly developed STA compensation technique was applied clinically to investigate three-dimensional (3D) knee joint kinematics of patients after anterior cruciate ligament (ACL) injury and reconstructive surgery. 3D knee joint kinematics of ACL-deficient patients and ACL-reconstructed patients was investigated during level walking and compared with a group of healthy subjects who had bilateral ACL-intact knees. Identification of biomechanical alterations during daily activities in ACL-deficient and ACL-reconstructed knees could help better understand clinical outcomes and seek improvement in surgical technique and rehabilitation regimen for ACL injury treatment.