## 4 Discussion  
The angle-position histories of the Simscape model and the mocap data shown in Figures 11 and 12 share a similar profile but the Simscape model has variations in the angle at various points of the simulation, which cause deviations in the path trajectories of the knee and ankle joints as shown in Figures 14 and 15. The path trajectories of the redesigned three-segmented leg assembly are close to the mocap data but feature deviations due to the angle difference between the mocap data and the leg assembly. The sources of the angle difference could be due to the lengths of the leg assembly segments remaining constant and the fact that the motion in the z-direction is not considered in our current work. However, the maximum deviations for the ankle and knee joints (8.3%, 4.8%) are smaller for the three-segment assembly than the two-segment assembly (11.8%, 6.1%) but maintained a similar profile to the mocap trajectory data. This shows promise for the redesigned leg assembly and can be further improved by resolving the deviation. A reason for this deviation could be due to the segment lengths remaining constant throughout the simulation. The mocap data reveal small shifts in the distance between the joints every single frame, which demonstrates small changes in the position of the joints that cannot be captured by having the leg segments at a constant length. These changes in segment length of the mocap data are caused by movement into the unmeasured axis, the z axis. If segment-length compensations were permitted by the Simscape model, the joint-path trajectories of the redesigned leg assembly could be nearly exact to the mocap data. Further work would therefore involve changing the leg segments to feature variable lengths using adjustable spring stiffness or other equivalent mechanisms and comparing the joint trajectories obtained to the mocap data. Implementation of variable-length leg segments in the future might be the key to replicating a more natural human gait motion and the exact replication of the joint trajectories.  
This work's impact extends to predicting and correcting gait motion and to joint angle and torque derivation. Such derived data could be used as a tool to assess the degree of paralysis that patients are suffering from. Furthermore, this derived data can also serve to prescribe specialized treatments to address specific problem areas.  
One of the promising applications of such data would be critical in mapping the path trajectory to the joints to develop smart implants with triboelectric patterns. Joint implants, such as for the knee, hip, and ankle, would therefore be able to signal their remaining life. Such a smart implant would alert physicians for replacement before it fails and injures the patient. The path trajectory mapping can also be applied to characterize asymmetry during gait by comparing relative motion in the left-right angle diagram [21] on the hip, knee, and ankle joints, respectively. This method may thus facilitate physicians' decisions in the  
Figure 15: Ankle-path trajectory from the mocap data and Simscape models of two/three segments.  
early diagnosis and in the evaluation of disease progression in diseases such as Parkinson's disease [22].