## 5 Experiments  
Experiments were conducted to test the proposed method by comparing it with two comparative methods (The experiments were approved as No. H2016001 by the Research Ethics Committee, Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui.). One wearer was a male student in his early 20s. In this experiment, he walked forward and backward for approximately 5 m wearing the powered exoskeleton. The data for the database were obtained while the powered exoskeleton was lifted by a gantry, and the wearer walked with the gantry so that he did not have any payload from the powered exoskeleton while his motion was restricted by the kinematics of the exoskeleton. Figure 6 shows how the data for the database were obtained for (a) \"walking forward\" and (b) \"walking backward\". The datasets for \"standing\" were also obtained when the wearer stood in upright position. The sampling time was approximately 80 milliseconds. The window size of the dataset was 10 steps. The number of datasets for each motion category was approximately 50. Once the database had been constructed, the proposed method was applied. The \(k\) of the k-NN was set to 5 for motion category recognition and 10 for appropriate joint angle estimation in the experiments. Figure 7 shows the results of the estimation of the wearer's motion based on the proposed method. The wearer first stood in the upright position, started walking forward, stopped, and stayed still there for a while; he then walked backward, and stopped. The figure shows that the proposed method successfully recognized the wearer's motion. The sampling time of the control system was approximately 80 milliseconds. The calculation of the motion recognition takes only about 20 milliseconds on the controller. The calculation of the whole control system including sensor value acquisition and motor control takes less than 80 milliseconds so that the powered exoskeleton assists the wearer's motion in real time. An additional 15 kg weight was placed on the powered exoskeleton in the experiments. The proposed method and the competitive methods described in Sections 4.1 and 4.2 were applied to the powered exoskeleton one by one across enough breaks for the wearer. Figures 8 and 9 show the image sequences of the motion \"walking forward\" and \"walking backward\" based on each method. The images were captured from a side. The motion category recognition worked perfectly based on  
Figure 6: Data acquisition for database construction: the wearer walks forward and backward wearing the powered exoskeleton while it is lifted by a gantry and pushes the gantry himself. Figure 7: Results of motion estimation based on k-nearest neighbor algorithm. the proposed method and the foot force switch-based method. Table 1 shows the walking speeds for \"walking forward\" and \"walking backward\" based on each method. Figure 8 shows that the proposed method and the foot force switch-based method enabled the wearer to walk smoothly while the gravity compensation-based method did not. The sampling time of image capture was approximately 1.8 s. The gravity compensation-based method caused the wearer to walk more slowly than the other methods. Table 1 also shows that the proposed and the force switch-based methods supported \"walking forward\". The wearer supported by the gravity compensation method slowly walked forward because this method does not actively support walking. Figure 9 shows that the proposed method allowed the wearer to walk backward faster than other methods. The proposed method recognized the wearer's motion of walking backward correctly and supported it appropriately. On the other hand, the foot force switch-based method caused the wearer to walk backward slowly because it tried to support him in walking forward even though he was walking backward. Eventually, the wearer needed to exert a strong force to push his leg backward and walk slowly.