## 6 Conclusions and Discussions  
This study proposed a power assist control system based on the wearer's estimated motion using motion sensors for a powered exoskeleton without leg binding. It recognizes the wearer's motion using motion sensors, estimates appropriate joint angles for the powered exoskeleton based on a motion database compiled in advance, and assists the wearer's motion in real time. The experimental results exhibited the effectiveness of the proposed assistive system.  
The major feature of our powered exoskeleton is that it does not bind the legs of the wearer. It allows the wearer to move his/her legs freely at the beginning of the motion even if the joints of the exoskeleton are fixed because of the room to move knees. The feature enables us to use motion sensors to recognize the wearer's motion and give feedback on the power assist. It supports only hip and knee joints rotating on the lateral direction. The other joints, for example, hip joints rotating in different directions and ankle joints are passive. The wearer needs power assists on those joints, too, if the load on the wearer increases more. It is one of the future works from the viewpoint of the mechanical design of the powered exoskeleton to strengthen the existing active joints and replace the passive joints to the active one.  
Figure 13: Answers to questionnaire on the powered exoskeleton controlled by the methods.  
In principle, the proposed method simply replays the pre-recorded joint angles from the database. However, even if the walking speed changes, it tries to find the best matching motion from the database to support it. If the wearer walks more slowly than the pre-recorded walk, the system tries to find the best matching phase of the walk and assists faster walking. If the wearer walks more quickly than the pre-recorded walk, it assists in walking slower but does not prevent the human walk because it always follows the walk to find the best matching phase based on the database. Therefore, it can adapt to a certain degree. If the walk is too far from the pre-recorded datasets, the proposed method fails to support it and needs a new dataset for walks at different speeds. This will form part of our future work.  
In this paper, we employed the k-nearest neighbors algorithm to deal with the motion database. The reason is that it does not make some specific assumptions on the motions of the human or powered exoskeleton and it is the simplest method among the various machine learning technique. However, there is a possibility to employ the other sophisticated algorithm. We are investigating more effective algorithms for motion learning [27; 28]. Another part of our future work in this area will involve increasing the variety of motions that can be assisted, that is, not only standing and walking motions and forward-backward motions, but also sideways walking, squatting, swinging the body, climbing stairs, and so on. We also intend to investigate online updates of the motion database.  
**Author Contributions:** conceptualization, S.N. and Y.T. ([PERSON]); methodology, S.N. and Y.T. ([PERSON]); software, S.N.; validation, S.N., K.S. and S.M.; formal analysis, S.N. and S.M.; investigation, S.N., K.S., S.M., and Y.T. ([PERSON]); resources, Y.T. ([PERSON]), M.K., Y.T. ([PERSON]), T.N.; data curation, S.N., K.S. and S.M.; writing--original draft preparation, Y.T. ([PERSON]); writing--review and editing, Y.T. ([PERSON]); visualization, S.N., K.S. and S.M.; supervision, Y.T. ([PERSON]), Y.T. ([PERSON]), and T.N.; project administration, M.K.; funding acquisition, M.K.  
**Funding:** This work was supported by research grants from the Fukui Prefectural Government, Japan.  
**Conflicts of Interest:** The authors declare no conflict of interest.