An EMG signal shows almost one-to-one relationship with the corresponding muscle. Therefore, each joint motion can be estimated relatively easily based on the EMG signals to control wearable robots. However, necessary EMG signals are not always able to be measured with every user. On the other hand, an EEG signal is one of the strongest candidates for the additional input signals to control wearable robots. Since the EEG signals are available with almost all people, an EEG based method can be applicable to many users. However, it is more difficult to estimate the user's motion intention based on the EEG signals compared with the EMG signals. In this paper, a user's motion estimation method is proposed to control the wearable robots based on the user's motion intention. In the proposed method, the motion intention of the user is estimated based on the user's EMG and EEG signals. The EMG signals are used as main input signals because the EMG signals have higher correlation with the motion. Furthermore, the EEG signals are used to estimate the part of the motion which is not able to be estimated based on EMG signals because of the muscle unavailability.

In the case of some elderly or disabled persons, not only the motor ability, but also the environment perception ability is sometimes deteriorated. To assist the daily living motion of those people, power-assist robots with the perception-assist have been proposed. The power-assist robot with the perception-assist assists not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. Since it is difficult for the robot to prepare all proper perception-assist for every task, tool, and environment previously, the robot needs to learn the proper perception-assist for each task, tool and environment by itself. The effectiveness of the performed perception-assist by the robot has been judged by the EMG signals. However, if the EMG signals do not change enough for the judgment, the learning of the robot might not succeed. In this paper, both EMG signals and EEG signals are measured at the same time to observe the features of these signals when users use the power-assist robot. EEG signals are used as the criteria of the effectiveness of the performed perception-assist in addition to EMG signals.

Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.

In the case of some physically weak persons such as elderly persons, the environment perception ability is sometimes deteriorated also. To assist the daily living motion of those people, power-assist robots with the perception-assist have been proposed. The power-assist robot with the perception-assist assists not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. In the case of lower-limb motion, the walking is the very important motion for a person to achieve daily activities. Some elderly persons might stumble on the bump and fall down in the stairs because they might not be able to lift own lower-limb well and/or recognize stairs correctly. In this paper, specifically, the stairs-ascending/descending assist for a lower-limb power-assist robot is proposed to prevent the user from falling down in stairs. ZMP of the user is taken into account in the proposed method. The effectiveness of the proposed method has been evaluated by the performing experiments.

In the case of some elderly whose motor ability is deteriorated, the environment perception ability is sometimes deteriorated also. In order to deal with that problem, the power-assist robot with the perception-assist, which assists not only the user's motion but also the user's interaction with an environment, has been proposed. The power-assist robot with the perception-assist has some sensors in order to monitor the interaction between the user and the environment. The robot tries to modify the user's motion automatically during the power-assist if the robot recognizes any problems in the user's motion. A human has a dexterous hand with many DOFs, and performs tasks while using many tools in the hand. Since many kinds of tools are used in daily activities, the undesired motion such as a risky behavior to the person is different between the tasks or tools. From these reasons, when the power-assist robot carries out the perception-assist, the robot needs to perform the perception-assist based on the user's motion and the grasped tool by the user. However, it is not realistic to prepare the perception-assist required for every tool or task because there are many kinds of tools and tasks. In this paper, the perception-assist for an upper-limb power-assist robot is discussed. The robot learns the properly required perception-assist based on EMG signals. The effectiveness of the proposed method has been evaluated by the performing experiments.