In recent years, research and development of human-assist robots equipped with brain-computer interfaces (BCIs) have accelerated due to the increasing number of stroke cases worldwide, which are a major contributor to permanent disabilities and loss of independence. BCIs can be an effective neurorehabilitation tool, as they assist the impaired sensorimotor loop by providing compensatory somatosensory feedback during motor attempts. The human-assist robot must support the user’s movements based on their intended actions. However, determining whether the robot’s assist motion is as intended by the user in real time remains challenging. This paper proposes a multi-stage method that utilizes time-frequency and statistical analyses for electroencephalography (EEG) signals to detect whether the arm movements are as intended. Furthermore, it employs a binary classifier with threshold optimization—guided by the highest classification accuracy— to determine the most effective threshold for distinguishing between intentional and unintentional movements. The effectiveness of the proposed method was evaluated through experiments involving multiple arm movements that combine both simple and complex tasks. A disturbance mechanism device was built and employed during these experiments to generate the sense of motion mismatch by applying disturbance forces. The experimental results demonstrate the effectiveness of the proposed method in detecting motion mismatch and distinguishing between intentional and unintentional movements. The proposed method is applicable to human-assist robots to assess whether the robot’s assist movement matches the user’s intended motion.

Stabilizing walking motion is important to avoid unexpected falling for physically weak persons such as elderly. It is known that light touch makes human posture stable. This paper presents the way to realize the effect of light touch to make human walking stable using an omnidirectional mobile robot. The mobile robot has been developed to keep light touch contact to the lower back of the user during walking. In this study, the proper amount of light touch force for lower back and its contact location are investigated at first. Then it is realized by the mobile robot while the user is walking. The effectiveness of the proposed robotic light touch method was evaluated by performing the experiment.

Artificial motion sensation can be used for virtual reality, rehabilitation, etc. It is known that joint motion illusion is induced by giving certain vibration stimulation on human muscles even though the joint is not actually moved. In this paper, a method to generate motion illusion of arbitrary hand reaching motion, which is one of the most important daily living movements, by giving multiple vibration stimulations on shoulder and elbow muscles is proposed. In order to generate hand reaching motion illusion, frequency of vibration stimulation is controlled on each muscle at the same time considering the spatial hand movement in the proposed method. The experimental of generating hand reaching motion illusion is carried out to evaluate the effectiveness of the proposed method.

Engineering support for sports has the potential to improve training performance. Most research on sports training support has focused on support in areas that directly affect the point of force action. On the other hand, even if sports with hand-held tools, the importance of lower body motor function has been indicated. This suggests that supporting areas away from the point of force action can improve the performance of novice players. Therefore, in this study, we developed an assist suit that facilitates lower body twisting for table tennis beginners during forehand swing. U sing this assistive suit, experiments were conducted for the experimental group under the following four conditions (1) “No wear (before),” (2) “Without assist,” (3) “With assist,” and (4) “No wear (after)” to clarify the effect of wearing, assist, and training. In addition, a control group in which participants hit the ball without the assist suit was set up to examine the training effect of repetition. The results showed that both the amount of waist rotation and racket velocity increased significantly in the experimental group compared to the control group, which indicates the effect of the assist suit on training.

For universal robotic limbs, having a large workspace with high stiffness and adjustable output properties is important to adapt to various situations. A combination of parallel mechanisms that can change output characteristics is promising to meet these demands. As such, we propose a lever mechanism with double parallel link platforms. This mechanism is composed of a lever mechanism with the effort point and the pivot point; each is supported by a parallel link mechanism. First, we calculated the differential kinematics of this mechanism. Next, we investigated the workspace of the mechanism. The proposed mechanism can reach nearer positions than the posture with the most shrinking actuators thanks to the three-dimensional movable effort point. Then, we confirmed that this mechanism could change the output force profile at the end-effector by changing the lever ratio. The main change is the directional change of the maximum output force. The change range is larger when the squatting depth is larger. The changing tendency of the shape of the maximum output force profile by the position of the pivot plate depends on the force balance of the actuators. These analytical results show the potential of the proposed mechanism and would aid in the design of this mechanism for robotic limbs.

Human-assist robots are designed to reduce the burden on the body when lifting objects and assist the elderly, disabled, and other people with muscle weakness in their daily activities. They also avoid accidents and assist motions by recognizing what kind of motion the user is about to make. Therefore, human-assist systems need to estimate the motion intention of a user in real time. The sooner the assist robot can accurately recognize the user’s motion, the sooner the assist robot can plan which motion to assist and how to assist it to guarantee success. If the user’s intended motion and its phase are estimated in the early stage of the motion, the assist robot can Figure out how the user is moving by comparing with standard motion models to assist the motion as necessary. This paper proposes a method to estimate the user’s intended motion and its phase simultaneously in real time based on integrated information consisting of the user’s posture, motion, EMG signals, and the surrounding environment. Two kinds of artificial neural networks are applied in the proposed method. Damping neurons are used in the artificial neural network to estimate the motion phase effectively. The intended lower-limb motions and their phases in daily living motion are estimated in real-time. The effectiveness of the proposed method was evaluated by performing experiments of lower-limb motion.

We propose ThermoCaress, a haptic device to create a stroking sensation on the forearm using pressure force and present thermal feedback simultaneously. In our method, based on the phenomenon of thermal referral, by overlapping a stroke of pressure force, users feel as if the thermal stimulation moves although the position of temperature source is static. We designed the device to be compact and soft, using microblowers and inflatable pouches for presenting pressure force and water for presenting thermal feedback. Our user study showed that the device succeeded in generating thermal referrals and creating a moving thermal illusion. The results also suggested that cold temperature enhance the pleasantness of stroking. Our findings contribute to expanding the potential of thermal haptic devices.

The physical human–robot interaction (pHRI) research field is expected to contribute to competitive and cooperative human–robot tasks that involve force interactions. However, compared with human–human interactions, current pHRI approaches lack tactical considerations. Current approaches do not estimate intentions from human behavior and do not select policies that are appropriate for the opponent’s changing policy. For this reason, we propose a reinforcement learning model that estimates the opponent’s changing policy using time-series observations and expresses the agent’s policy in a common latent space, referring to descriptions of tactics in open-skill sports. We verify the performance of the reinforcement learning agent using two novel physical and competitive environments, push-hand game and air-hockey. From this, we confirm that the latent space works properly for policy information because each latent variable that represents the machine agent’s own policy and that of the opponent affects the behavior of the agent. Two latent variables can clearly express how the agent estimates the opponent’s policy and decides its own policy.

It is essential for a monitoring system or a communication robot that interacts with an elderly person to accurately understand the user's state and generate actions based on their condition. To ensure elderly welfare, quality of life (QOL) is a useful indicator for determining human physical suffering and mental and social activities in a comprehensive manner. In this study, we hypothesize that visual information is useful for extracting high-dimensional information on QOL from data collected by an agent while interacting with a person. We propose a QOL estimation method to integrate facial expressions, head fluctuations, and eye movements that can be extracted as visual information during the interaction with the communication agent. Our goal is to implement a multiple feature vectors learning estimator that incorporates convolutional 3D to learn spatiotemporal features. However, there is no database required for QOL estimation. Therefore, we implement a free communication agent and construct our database based on information collected through interpersonal experiments using the agent. To verify the proposed method, we focus on the estimation of the "mental health" QOL scale, which is the most difficult to estimate among the eight scales that compose QOL based on a previous study. We compare the four estimation accuracies: single-modal learning using each of the three features, i.e., facial expressions, head fluctuations, and eye movements and multiple feature vectors learning integrating all the three features. The experimental results show that multiple feature vectors learning has fewer estimation errors than all the other single-modal learning, which uses each feature separately. The experimental results for evaluating the difference between the estimated QOL score by the proposed method and the actual QOL score calculated by the conventional method also show that the average error is less than 10 points and, thus, the proposed system can estimate the QOL score. Thus, it is clear that the proposed new approach for estimating human conditions can improve the quality of human-robot interactions and personalized monitoring.

The dolphin kick is a swimming style characterized by undulation of the body. As a platform for swimming research, we have developed a musculoskeletal humanoid robot called Triton. Triton has a flexible spine with erector spinae muscles and a stiffness adjustment system for lumbar joints. The musculoskeletal body includes biarticular and polyarticular muscles, providing multi-joint coordination. The robot is actuated by pneumatic muscles, yielding lightweight and inherently waterproof properties. The compliance of the joints allows interactions between body and fluid similar to those of human swimming. This study presents the design concept of Triton and experimental results from a water tank test. We compare the results with simulation and human movements reported in literature. The results show that the musculoskeletal swimming robot has similar cycle trends in joint angle and thrust force.

Systems with changeable mechanical properties show promise for expanding the applications of dynamic robots. We proposed the Torque-Angle Relationship Control System (TARCS) for musculoskeletal robots with changeable output properties. First, we formulated TARCS and examined its static properties. Next, we used TARCS to change the properties and investigated the effect of the same on the jumping ability through simulations. We found that TARCS in a biarticular muscle determined the general shape of the jumpable range. Furthermore, TARCS in a monoarticular muscle could change the amplitude of the jumpable range. Both TARCS also changed the directional property of the reaction to the disturbance. Finally, we developed a musculoskeletal quadruped robot in which TARCS was implemented. This robot could change its jumping direction using TARCS, and it jumped to a height of 0.254 m at the lowest point of its body.

For robots using elastic devices, pole vault is a particularly interesting task because poles have large differences from previously studied elastic elements in terms of their elastic capacity. The active actuation of the agent in “pole support phase” plays important roles in improving vaulting performance. Investigating this actuation can contribute to the design of novel control strategies during the time when the agent contacts with environment through the elastic device. In this study, we specifically examined an active bending effect performed in the “pole support phase.” We analyzed the active bending effect on reachable height (vaulting height) using the “Transitional Buckling Model.” We applied this active bending theory to a robot and verified the active bending effect to improve vaulting height. Results show that active bending motion in the “pole support phase” improves the pole vault performance and that the timing of the bending direction change is an important factor for defining the vaulting performance. These results will facilitate the application of robots using large elasticity.

To gain a synthetic understanding of how the body and nervous system co-create animal locomotion, we propose an investigation into a quadruped musculoskeletal robot with biologically realistic morphology and a nervous system. The muscle configuration and sensory feedback of our robot are compatible with the mono- and bi-articular muscles of a quadruped animal and with its muscle spindles and Golgi tendon organs. The nervous system is designed with a biologically plausible model of the spinobulbar system with no pre-defined gait patterns such that mutual entrainment is dynamically created by exploiting the physics of the body. In computer simulations, we found that designing the body and the nervous system of the robot with the characteristics of biological systems increases information regularities in sensorimotor flows by generating complex and coordinated motor patterns. Furthermore, we found similar results in robot experiments with the generation of various coordinated locomotion patterns created in a self-organized manner. Our results demonstrate that the dynamical interaction between the physics of the body with the neural dynamics can shape behavioral patterns for adaptive locomotion in an autonomous fashion.

Because of the complexity of the Japanese language, it is not easy to prepare captioned video tapes for deaf persons. In order to make Japanese captioning easy, quick, and faithful to speech, we developed and tested a new, convenient system that consists of a Japanese word processor, a superimposing device with a special-purpose interface, and a set of commercial video equipment. A brief description of the developed system is given, together with the results obtained.