Recently, the importance of mechanical transparency in human-assistive robots has grown. Traditionally, its primary goal was minimizing interaction forces during assistance. However, under this conventional definition, mechanical transparency was not considered when an interaction force was required during assistance. This research focuses on achieving mechanical transparency within the context of shoulder motion in upper extremity exoskeletons for rehabilitation. Our primary goal is maintaining interaction forces at target values, even with motion disturbances. To this end, we developed a shoulder actuation testbed for exoskeletons, incorporating a fusion hybrid linear actuator distinguished by high back-drivability, robust torque generation capability, and safety features. To attain mechanical transparency, we created a model for calculating the required joint torque, accounting for gravitational dynamics, and subsequently determined the necessary actuator output. The system characteristics were evaluated based on the joint torque generated by the actuator. The actuator utilized pneumatic pressure to generate force and compensated for kinetic friction using electromagnetic forces. The results showed that the compensation by the electromagnetic force reduced the root mean square error of the torque to less than 60% in relation to pneumatic pressure alone. This demonstrated the ability to generate consistent torque with high robustness to motion disturbances.

Previous works in the literature have claimed that the characteristics of electromyography (EMG) signals depend on each person, and thus, EMG interfaces need to be carefully calibrated for each user in myoelectric control. In this study, we show that the EMG interface used to estimate the joint torques of a user can be constructed simply by incorporating other users’ data without typical calibration process. To achieve this plug-and-play capability, we introduce the concept of collaborative filtering to estimate the joint torque of a novel user by exploiting the preidentified relationships between motion-body features, including EMG signals, and the joint torques of other users. To validate our proposed approach, we compare the performance of estimating joint torque by the proposed method with that by conventional linear regression models as a baseline. We considered the following two baseline methods.Linear-own:The parameters of the linear model are calibrated for each subject from his/her own training data.Linear-others:The parameters of the linear model are calibrated with the other users’ data in which the novel user's data are not included. As a result, the estimated joint torques from our proposed approach reveal a better estimation performance than those from the baseline approaches. Furthermore, we also successfully demonstrate online myoelectric control of an upper limb exoskeleton robot with an attached mannequin arm.

In this study, we propose a novel human-in-the-loop optimization approach for exoskeleton robot control. We develop a method to optimize widely-used Electromyography (EMG)-based assistive strategies. If we use multiple EMG channels to control multi-DoF robots, optimization process becomes complex and requires a large amount of data. To make the optimization tractable, we exploit the synergies both of the human muscles and artificial muscles of the exoskeleton robots to reduce the number of parameters of the assistive strategies. We show that we can extract the synergies not only from the user's muscle activities but from pneumatic artificial muscle (PAMs) contractions of the exoskeleton robot. Then, we adopt a Bayesian optimization method to acquire the parameters for assisting human movements by iteratively identifying the user's preferences of the assistive strategies. We conducted experiments to evaluate our proposed method with a PAMs-driven upper-limb exoskeleton robot. Our method successfully learned assistive strategies from the human-in-theloop optimization with a practicable number of interactions.

This paper introduces an approach that robotizes an ankle-foot orthosis (AFO). In particular, toward post-stroke gait rehabilitation, we robotize a double-bar AFO, which is widely used in rehabilitation facilities, by newly designing a modular joint, a pneumatic actuator, and a Bowden cable force-transmission system. Our modular joint system, called the Modular Exoskeletal Joint (MEJ), has a hollow shaft for simple attachment to an AFO's pivot. We designed MEJ to compactly house an encoder that is built in a bearing in a pulley. We adopted Bowden cables to transmit contraction forces from an actuator to the MEJ. As an actuation scheme, we developed the Nested-cylinder Pneumatic Artificial Muscle (NcPAM) system. Even though PAMs are mechanically compliant and lightweight, they can still generate a large force. Therefore, they can provide an ideal actuation system for exoskeletal robots. The nested-cylinder in NcPAM houses a cable-tensioning spring to properly maintain small cable tension for passive movements and a cable stopper to connect the PAM and the cable for properly transmitting the large force generated by PAM. We show the ankle-joint trajectory tracking performances of this integrated system using iterative learning control.

Since exoskeletons show potential for rehabilitation therapy, many scientists have been designing upper extremity exoskeletons. Unfortunately, few have successfully provided a shoulder exoskeleton for severe impairment. Toward Brain-Machine-Interface (BMI) rehabilitation robot therapies for severe upper extremity impairment, this paper introduces a shoulder exoskeleton robot with a modular joint and an off-board modular actuator. We applied a Modular Exoskeletal Joint (MEJ) to a shoulder exoskeleton that was driven by Pneumatic Artificial Muscles (PAMs) transmitted by a Bowden cable. Our objective is generating passive movements triggered by BMI. Since large torque has to be generated for assist a whole arm, we newly designed a more powerful Nested-cylinder PAMs (NcPAMs) than our previous work. As a proof of the concept of the mechatronics design, we show the tracking performance of the periodic trajectory of a joint angle with both human and mannequin arms as simulated impairments. Our result shows that the tracking error is sufficiently small in all of the conditions and that our developed shoulder exoskeleton is an adequate substitute for flexion/extension movements.

Haptic guidance is a motor training procedure in which the subject is physically guided through an ideal motion by an haptic interface. It is especially valuable for repetitive training, and more effective when coupled with additional sensory feedback, e.g. the visual modality. The advantage of additional feedback modalities may stem from the learner's increased active participation and engagement. Here, we test this hypothesis by analyzing the learners' brain state during haptic training. Specifically, we focus on the sensorimotor rhythms (SMR), since they have been previously associated with the level of engagement directed towards a motor task. We conducted a circle-drawing haptic training, where subjects were asked to memorise the guided trajectory while their electroencephalogram (EEG) was recorded. During the experiment, only the haptic modality was maintained (i.e. unimodal) by keeping the task workspace visually hidden. Results show a clear trend: subjects who exhibited a performance improvement, were characterized by a stronger desynchronization of the beta rhythms over the contralateral hemisphere. This is in agreement with recent studies showing that contralateral beta rhythms changes are associated with motor skills retention. Moreover, under the assumption that SMR are indeed a marker of engagement, our results represent accumulating evidence that active participation is crucial for haptic training.

Recent studies suggest that reinforcement learning has great potential for generating assistive strategies in exoskeletons through physical interactions between a user and a robot. Previous methods focused on a task-specific assistive strategy, where for every single task (situation/context), the user needs to interact with a robot to learn an appropriate assistive strategy. Therefore, the learned strategies cannot be generalized for a new task. Since the sampling cost is expensive for such human-in-the-loop systems as exoskeletons, generalization must be enabled. In this paper, we propose to learn task-parametrized assistive strategies for exoskeleton robots. Our method employs an assistive strategy, which depends on the task parameter and the state variable, that can be learned from multiple sets of human-robot interaction data across different tasks and generalized even for an unseen task, given the task parameter without additional learning. To alleviate the user's burden in the learning process across multiple tasks, we exploit a data-efficient multi-task reinforcement learning framework. To verify the effectiveness of our method, we developed an experimental platform with an exoskeleton robot. We conducted a series of experiments whose experimental results show that our method can learn such a task-parametrized assistive strategy and be generalized for unseen tasks to reduce the user's electromyography signals (EMGs) during tasks.

Pneumatic Artificial Muscle (PAM) actuators have been used as exoskeletons because of their inherited compliance and high power-weight ratio. However, creating accurate models remains difficult mainly due to the compliance issue; the model can be changed by the force applied by the user. Therefore, both user and robot actions need to be considered for sufficient excitation of PAMs that are equipped in exoskeleton robots, unlike typical rigid actuators that can only be sufficiently excited by robot actions. In this paper, we propose a user-robot collaborative excitation approach for PAM model identification as an active learning framework for sequentially collecting data by deriving and executing optimal user and robot actions at each step with Gaussian processes. The optimal actions, which are executed by the robot, are displayed on a monitor that enables the user to execute them. We conducted experiments using a powered elbow exoskeleton with a PAM actuator. Experimental results show that our method can more efficiently identify the PAM model than a standard model identification method that does not use any data acquired through user-robot collaboration.

Social demand for exoskeleton robots that physically assist humans has been increasing in various situations due to the demographic trends of aging populations. With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. In this paper, we explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user’s muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies. The key underlying assumption is that the user is instructed to perform the task by his/her own intended movements. Since the EMGs are observed when the intended movements are achieved by the user’s own muscle efforts rather than the robot’s assistance, EMGs can be interpreted as the “cost” of the current assistance. We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. We also showed that our proposed method can cope with changes in both the robot dynamics and movement trajectories.

In this study, we propose a human movement model both for myoelectric assistive robot control and biosignal-sensor-failure detection. We particularly consider an application to upper extremity exoskeleton robot control. When using electromyography (EMG)-based assistive robot control, EMG electrodes can be easily disconnected or detached from skin surfaces because the human body is always in contact with the robot. If multiple electrodes are used to estimate multiple joint movements, the probability of sensor electrode misplacement increases due to human error. To cope with the aforementioned issues, we propose a novel human movement estimation model that takes anomalies into account as uncertain observations. We estimated human joint torques by automatically modulating the contribution of each sensor channel for the movement estimation based on anomaly scores that were computed according to synergistic muscular coordination. We compared our proposed method with conventional approaches during drinking-movement estimation with five healthy subjects in the three aforementioned anomaly situations and showed the effectiveness of our proposed method. We applied it to a four-DOF upper limb assistive exoskeleton robot and showed proper control in sensor failure situations.

Designing an assistive strategy for exoskeletons is a key ingredient in movement assistance and rehabilitation. While several approaches have been explored, most studies are based on mechanical models of the human user, i.e., rigid-body dynamics or Center of Mass (CoM)-Zero Moment Point (ZMP) inverted pendulum moECenter of Massdel, or only focus on periodic movements with using oscillator models. On the other hand, the interactions between the user and the robot are often not considered explicitly because of its difficulty in modeling. In this paper, we propose to learn the assistive strategies directly from interactions between the user and the robot. We formulate the learning problem of assistive strategies as a policy search problem. To alleviate heavy burdens to the user for data acquisition, we exploit a data-efficient model-based reinforcement learning framework. To validate the effectiveness of our approach, an experimental platform composed of a real subject, an electromyography (EMG)-measurement system, and a simulated robot arm is developed. Then, a learning experiment with the assistive control task of the robot arm is conducted. As a result, proper assistive strategies that can achieve the robot control task and reduce EMG signals of the user are acquired only by 30 seconds interactions.

This paper presents a biologically-inspired real-time balance recovery control strategy that is applied to a lower body exoskeleton with variable physical stiffness actuators at its ankle joints. For this purpose, a torsional spring-loaded flywheel model is presented to encapsulate both approximated angular momentum and variable physical stiffness, which are crucial parameters in describing the postural balance. In particular, the incorporation of physical compliance enables us to provide three main contributions: i) A mathematical formulation is developed to express the relation between the dynamic balance criterion ZMP and the physical ankle joint stiffness. Therefore, balancing control can be interpreted in terms of ankle joint stiffness regulation. ii) ‘Variable physical’ stiffness is utilized in the bipedal robot balance control task for the first time in the literature, to the authors' knowledge. iii) The variable physical stiffness strategy is compared with the optimal constant stiffness strategy by conducting experiments on our exoskeleton robot. The results indicate that the proposed method provides a favorable balancing control performance to cope with unperceived perturbations, in terms of center of mass position regulation, ZMP error and mechanical power.

In this paper, we propose an estimation method of human joint movements from measured EMG signals for assistive robot control. We focus on how to estimate joint movements using multiple EMG electrodes even under sensor failure situations. In real world applications, EMG sensor electrodes might become disconnected or detached from skin surfaces. If we consider EMG-based robot control for assistive robots, such sensor failures lead to significant errors in the estimation of user joint movements. To cope with these sensor failures, we propose a state estimation model that takes uncertain observations into account. Sensor channel anomalies are found by checking the covariance of the EMG signals measured by multiple EMG electrodes. To validate the proposed control framework, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow joint movement estimation. We show proper control of a one-DOF exoskeleton robot based on the estimated joint torque using our proposed method even when one EMG electrode has a sensor problem; a standard method with no tolerability against uncertain observations was unable to deal with these fault situations. Furthermore, the errors of the estimated joint torque with our proposed method were smaller than the standard method or a method with a conventional sensor fault detection algorithm.

In this paper, we propose an optimal control framework for pneumatic actuators. In particular, we consider using Pneumatic Artificial Muscle (PAM) as a part of Pneumatic-Electric (PE) hybrid actuation system. An optimal control framework can be useful for PE hybrid system to properly distribute desired torque outputs to the actuators that have different characteristics. In the optimal control framework, the standard choice to represent control cost is squared force or torque outputs. However, since the control input for PAM is pressure rather than the force or the torque, we should explicitly consider the pressure of PAM as the control cost in an objective function of the optimal control method. We show that we are able to use pressure input as the control cost for PAM by explicitly considering the model which represents a relationship between the pressure input and the force output of PAM. We demonstrate that one-DOF robot with the PE hybrid actuation system can generate pressure-optimized ball throwing movements by using the optimal control method.

In this paper, we introduce our ongoing work on the development of an upper body exoskeleton robot, driven by a pneumatic-electric hybrid actuation system. Since the limb of an exoskeleton robot needs to have small inertia to achieve agility and safety, using a heavy actuator is not preferable. Furthermore, we need to use backdrivable actuators that can generate sufficiently large torques to support user movements. These two requirements may seem contradictory. In order to cope with this development problem, we use a hybrid actuation system composed of Pneumatic Artificial Muscles (PAMs) and small-size electromagnetic motors. Although we and other research groups have already presented the advantage of the hybrid actuation system, we newly propose the usage of Bowden cable in a hybrid actuator to transmit the force generated by the PAMs to joints of our exoskeleton robot so that we can design a compact upper limb with small inertia. In addition, small size electric motors are mechanically connected to joints in order to compensate uncertainty generated by the PAM dynamics and the Bowden cable. We demonstrate that the proposed joint is backdrivable with the capability of large torque generation for the gravity compensation task both in One-DOF system with a dummy weight and right arm of the upper body exoskeleton with a mannequin arm. We also show the right arm exoskeleton can be moved using a torque input, extracted from sensory information via a goniometer.

This study proposes the design of electromyography (EMG)-based force feedback controller which explicitly considers human-robot interaction for the exoskeletal assistive robot. Conventional approaches have been only consider one-directional mapping from EMG to control input for assistive robot control. However, EMG and force generated by the assistive robot interfere each other, e.g., amplitude of EMG decreases if limb movements are assisted by the robot. In our proposed method, we first derive the nonlinear mapping from EMG signal to muscle force for estimating human joint torque, and convert it to assistive force using human musculoskeletal model and robot kinematic model. Additionally the feedforward interaction torque is feedback into torque controller to acquire the necessity loads. To validate the feasibility of the proposed method, assistive One-DOF system was developed as the real equipment and the simulator. We compared the proposed method with conventional approaches using both the simulated and the real One-DOF systems. As the result, we found that the proposed model was able to estimate the necessary torque adequately to achieve stable human-robot interaction.

We introduce our Pneumatic-Electric (PE) hybrid actuator model and propose to use the model to derive a controller for the hybrid actuation system by an optimal control method. Our PE hybrid actuator is composed of Pneumatic Artificial Muscle (PAM) and an electric motor. The PE hybrid actuator is light and can generate large torque. These properties are desirable for assistive devices such as exoskeleton robots. However, to maximally take advantage of PE hybrid system, we need to reasonably distribute necessary torque to these redundant actuators by properly taking distinctive characteristics of a pneumatic actuator and an electric motor into account. To do this, in this study, we use an optimal control method called iterative LQG to reasonably distribute the necessary torque to the PAM and the electric motor. The crucial issue to apply the optimal control method to the PE hybrid system is PAM modeling. We built a PAM model composed of three elements: 1) an (air)pressure-force conversion model, 2) a contraction rate model, 3) time delay of the air valve, and 4) the upper limit of force generation that depends on the contraction rate and the movable range. We apply our proposed method to a one degree of freedom (one-DoF) arm with PE hybrid actuator. The one-DoF arm successfully swing tasks 0.5 Hz, 2 Hz and 4 Hz and swing up and stability task by reasonably distributing necessary torque to the two different actuators in a simulated and a real environments.