One of the recent developments in physical reservoir computing, which uses the complex dynamics of a physical system as a computational resource, is the use of a pneumatic pipeline system as a computational resource. This uses the dynamics of air for computation, and because it is lightweight and power-saving, it is used for gait-assist control using a soft exoskeleton with pneumatic rubber artificial muscles. In this study, we verified that by feeding back the estimated information to a pneumatic pipeline system, the pneumatic physical reservoir computing can generate periodic pressure changes as a stable limit cycle, such as those seen in walking. A pneumatic reservoir with feedback loops was modeled to generate limit cycles in the simulation, and it was confirmed that the system could generate limit cycles with high accuracy even from initial positions far from the target limit cycle. This system is expected to be applied to assist walking movements with a soft exoskeleton with a lightweight computational device.

Wearable gait assistive devices have been im-proved by soft robotics in terms of safety and physical burden of the wearer. Currently, electrical sensors and computers around the wearer are bottlenecks in enhancing the wearer's activities. In this paper, we present a wearable gait assistive system using soft pneumatic system for all the actuation, sensing, and computation. Pneumatic artifical muscles (PAMs) on the thighs were used to generate assistance force, and thin PAMs on the whole legs were used for sensing the wearer's motion. All thin PAMs were connected to each other by tubes, and the pressure response in the tubes were exploited to compute the wearer's motion in manner of physical reservoir computing. The left thigh angular velocity estimated from the reservoir response was used for control the PAMs for actuation. Our experiment with the use of gait assistance showed that the system worked correctly. This paper shows that the pneumatic analog computation system for sensing soft body can help the functionality of soft wearable assistive devices.

Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements. In this study, we applied the model to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand. Without a priori knowledge about the arm and environment, the model was able to adapt to the force field by generating counteracting forces to overcome it in a manner similar to what is reported in the behavioral literature. The kinematics of the movements generated by our model share characteristic features of human movements observed before and after force field adaptation. In addition, we demonstrate that the structure and learning algorithm introduced in our model induced a shift in the end-point's equilibrium position and a static force modulation, accompanied by a fast and a slow learning process. Importantly, our model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment. Our model demonstrates a possible mechanism through which the central nervous system may control and adapt a point-to-point reaching movement without specifying a desired trajectory by continuously updating the body's musculoskeletal model.

In this study, we developed a selective driving joint forceps (SDJF) for laparoscopic surgery. The SDJF has a mechanism that the driving joints can be selected arbitrarily, therefore each joint doesn't require an individual actuator for operating. The developed SDJF has six joints that can be operated using only four actuators. Each joint has 2-degrees-of-freedom (DOF) of flexion. Therefore, the SDJF has the same working area as the forceps having six driving joints (each joints can bend ±30° around the X and Y axes). The mechanism of the SDJF is realized by fixing each joint with a collar made of shape memory polymer. The proposed mechanism not only reduces the number of actuators required for joint operation, but also has the rigidity of the forceps, which is important in surgery. In addition, a driving section of the forceps is driven by pneumatic cylinders, therefore, the forceps joint has high-back-drivability, lightweight and high-output. We measured the heating and cooling time required to change the driving joint, dynamic response and rigidity of the prototype SDJF.

In this study, a robotic forceps with a wrist joint using visual SLAM for joint angle sensing was developed. The forceps has a flexible joint connected to the wrist joint at its rear end and the motion of the rear joint is driven by a parallel linkage. A monocular camera attached on the rear of the parallel linkage is in charge of position sensing, and the joint angles are estimated from the pose of the camera. The pose of the camera is obtained by a visual SLAM. The visual servo system realizes a simple attaching mechanism. The static and dynamic positioning experiments are conducted. We confirmed that the visual servoing system controls the forceps tip within the error of 3 deg in the motion range of 50 deg.

A method to improve quality of life of elderly people is to increase muscle strength. The purpose of this study is to develop a machine to improve muscle strength effectively. In this paper, we proposed a muscle strength training machine that exerts variable resistance using pneumatic artificial rubber muscle. In addition, we observed the effect on muscle activity by using the proposed machine with predictable and unpredictable load change patterns. Experimental results showed that there was a difference between the rising time of muscle activity and the maximum amplitude of electromyogram depending on predictability of the load change and showed the possibility of effective muscle strength improvement against unpredictable load using the arm curl machine.

Motor skill learning is fundamental in many physical activities of human. In the processes of learning of motor skills, learners often receive visual or physical information about postures from teachers. However, the information about postures usually cannot be transmitted precisely. In this paper, we propose a motion teaching system to transmit teachers' motion to learners directly by using a motion capture and an assist suit. The assist suit, which has a pneumatic artificial rubber muscle (PARM) as an actuator, was designed to move a learner's hip joint with less loss of assistive force and less constraint of motion. Hip joint motion of a teacher can be transmitted to the assist suit by master-slave control. In addition, to compensate the delay of the PARM, posture of the teacher is predicted before the occurence by a recurrent neural network by using electromyogram signals and the past joint angle. We confirmed the system can transmit a teacher's motion to a learner in real time, and with the neural network, the delay of the learner's motion could be suppressed to approximately 0.1s, which is enough to feel visual and physical information synchronous. Therefore, the proposed motion teaching system would have the ability to transmit teachers' motion to learners visually and physically with precision sufficient to facilitate skill transmission.

Biological signals are used to control robotic exoskeletons. Our research group has developed a Brain-Machine Interface (BMI)-based Occupational Therapy Assist Suit (BOTAS), which can be controlled by both electroencephalography (EEG) and electromyography (EMG) signals. This paper presents a new method for estimating the joint angles of the arms and hands for the BOTAS from EMG signals using mathematical musculoskeletal models. In six able-bodied participants, the method succeeded in estimating the flexion/extension angles of the elbow, wrist, and metacarpophalangeal (MP) joint of the index finger. In a patient with an upper cervical spinal cord injury, the method succeeded in estimating the elbow angles. The participants could control the BOTAS empowered by the method. These results suggest that the method is potentially useful for controlling robotic exoskeletons in patients with paralyzed arms and fingers.