## 5 Discussion  
Table 2 summarizes the results of two experiments and presents a statistical analysis of the joint trajectories to demonstrate the effectiveness of using an exoskeleton system for basic ADLs. The data illustrated in Figure 7 show the variations in mean RMSE values among two tasks in joint space. Several parameters, such as mechanism design, mode of actuation, selection of control method to accommodate variations in payload, and human anthropomorphic parameters, may influence the functional reliability of the human-exoskeleton system.  
Implementation of a basic PD control method without gravity compensation/human arm weight compensation and a backdrivability of shoulder mechanism had made it difficult to achieve a more precise control compared to the other joint mechanisms. Therefore, average RMSE values for the shoulder joint were comparatively higher than the other two joints. [PERSON] et al. [1] implemented a PD control with gravity compensation and analyzed the effect of uncertain dynamics and external disturbances on the human-exoskeleton system. Data presented in [1,31,32] have shown that the performance of an exoskeleton driving human shoulder joint was comparatively lower than the elbow joint exoskeleton because the gravity torque induced by variable payload and human arm weight may affect  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Joints**} & **Average** & **Max** & **Min** & **Variance** & **Average** & **Max** & **Min** & **Variance** \\  & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** \\  & & **Drinking Task** & & & & & **Object****Picking Task** & \\ \hline Joint 1 & 0.0247 & 0.0382 & 0.0198 & 0.0184 & 0.0360 & 0.0402 & 0.0323 & 0.0079 \\ Joint 2 & 0.0210 & 0.0223 & 0.0196 & 0.0027 & 0.0146 & 0.0155 & 0.0131 & 0.0024 \\ Joint 3 & 0.0207 & 0.0238 & 0.0167 & 0.0071 & 0.0184 & 0.0213 & 0.0150 & 0.0062 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of PD control method using RMSE value for the performance assessment of the wheelchair exoskeleton.  
Figure 7: Bar diagram of the RMSE with variance from 16 trials for each task.  
the relative precision. Alternatively, the C-ring and worm gear mechanisms responsible for supporting a human shoulder joint rotation and elbow joint movements take advantage of a large reduction ratio. The design also facilitates holding the output position without energy consumption because of its non-backdrivability. Moreover, the two joints are relatively less affected by the variation in the payload and upper limb anatomy as can be seen from error bar diagram presented in Figure 7. [PERSON] et al. (2012) presented a similar mechanism and analyzed the position and orientation synchronization between the human upper limb and exoskeleton.  
The study demonstrates that the exoskeleton presented is applicable for motion assistance of physically impaired people in their ADLs. In our future work, we will extend this study to clinically evaluate the system to examine comfort, patient acceptance, and functional use of the system with severe to moderate upper limb impairment. We will look into more advanced control methods to compensate for ill effects caused by uncertain dynamics and external disturbances and study their implications for assistive applications. Manipulability/singularity free workspace is another important factor that should be considered during the path planning of an exoskeleton robot. Future work will focus on developing a method to optimize the exoskeleton's trajectory in the task space and attempt to maximize likelihood of manipulation in the high manipulability region; thereby, it guarantees uniform distribution of forces and the torques and improves the physical human-robot interaction.