## 6 Discussion  
From the results of the experiments in Section 3.3, it was found that there was a maximum operational efficiency for each surface and walking speed during stable walking, which demonstrates the validity of the hypothesis. According to the data from the four subjects, OAR was chosen as 2.0 for indoor, 3.5 for tile, and 3.7 for sand, respectively. Hence, the power-assisted cart needs to provide more assistance in a complex environment. In addition, we found that the performance index decreased with an increment in walking speed. Our experimental results also reveal that the operational efficiency values for the two proposed methods were relatively better than the ones for no assistance and default assistance. The OAR method had better operational efficiency for the indoor flat road, while DOB provided the most efficient assistance in most of the other cases. On the other hand, the improvement in efficiency with the DOB method resulted from the compensation to counteract disturbance and reducing the necessary force for pushing the cart, especially on a slope. However, with a load of 10 kg in the indoor environment, the operational efficiency for DOB was no better than that for the default assistance of \(\alpha=1.5\). This could be the result of using the low-pass filter since the difference between the actual plant and the nominal one was relatively small. In the experiments of this study, young subjects walked slowly to simulate the locomotion of the elderly. Since the step frequency was fixed in this study with the use of a metronome, increased walking speed corresponded to an increment in step length. The difference in dimensionless walking speed while walking upon the slope indicates that if DOB is active, the cart could easily be pushed even with a heavy load. The time histories of the operational forces for one trial with DOB with the same surface and participant as in Figure 4 without assistance is shown in Figure 11. The periodic variation related to gait became indistinct in comparison with that in Figure 4, while the necessary force to push the cart was drastically reduced. This implies that the force to move the cart was not only generated by the subject but also by the motor output. Both methods have some drawbacks. As shown in Section 3, the operational efficiency could be considered as a unimodal function of \(\alpha\) for a specific surface and walking speed, while the offline procedure in Section 5 for obtaining the OAR was very complex. Instead, the optimal one could be acquired via an on-line approach, and a golden-section search would be useful due to the relationship between the operational efficiency and assistance ratio. Moreover, the movement of walking on an unknown surface might result in high  
Figure 11: Operational forces of one subject with DOB in 15 s on an indoor surface. classification error and improper assistance from the cart, a problem that could be solved by simplifying the categories for classification. In addition, there is still a serious limitation while utilizing DOB. It was found that with DOB, when the user rotated on a slope, the cart became extremely hard to operate due to the rapid change in the disturbance. Thus, rotational movement on a slope needs to be detected and DOB must be turned off during rotation. In conclusion, each method has its merits and demerits. It seems better to utilize DOB in an unknown environment and OAR for indoor surfaces. The way of combining both methods may become accepted as a suitable strategy for solving the above problems. First, the surface is classified via a machine learning approach such as an SVM into only two categories: indoor and outdoor. The rotational movement and posture of the cart are detected simultaneously, then a proper method can be chosen according to the classification and detection results. For instance, the cart can be controlled by applying the OAR method for the indoor flat surface and switching to the DOB method for outdoor use. We combined OAR and DOB and implemented the above algorithm.