In recent years, inflatable actuators have been widely used in wearable suits to assist humans who need help in moving their joints. Despite their lightweight and simple structure, they have long inflation and deflation times, which make their quick use difficult. To resolve this issue, we propose an inflatable wrinkle actuator with fast inflation and deflation responses. First, a theoretical model is proposed to develop an actuator that satisfies the design requirements: the desired assistive torque and the foam factor based on the wearability. Second, we reduce the inflation and deflation times by partially controlling the actuator layers and by designing pneumatic circuits using a vacuum ejector. To validate the usability of the actuator in wearable suits, we applied it to a wearable knee suit, and the inflation and deflation times were 0.40 s and 0.16 s, respectively. As a result, we ensured that the actuator did not interfere with human knee joint movement during walking by creating any residual resistance.

Data-driven approach has been recently investigated for identifying human joint angles by soft sensors because it is possible to circumvent of difficulty of the modeling. However, this approach commonly requires high computational burden from the voluminous amount of data and the time-series oriented network architecture. Moreover, the nature of the soft sensors make the problem worse due to the inherent material nonlinearity and hysteresis. In this study, we developed a novel sensing sleeve design to minimize the nonlinearity and hysteresis of the soft sensors and used a computationally efficient calibration method based on transfer learning. Through attaching the soft sensors to a link rather than directly to a joint, the effects of external disturbances during joint motions were minimized. To calibrate the sensor to the body motions, transfer learning was used with the results from the open-source musculoskeletal simulation software (OpenSim) and the sensor data. Tracking accuracy of ankle motions using the proposed method significantly improved measurement accuracy and reduced calibration time for five healthy subjects. The proposed method could be used to calibrate the soft sensors with a shorter training time with more than 1000 times faster computation speed and while maintaining tracking accuracy with a lower amount of data.

This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture.

Wearable ground reaction force (GRF) measurement systems make it possible to measure the GRF in any environment, unlike a commercial force plate. When performing kinetic analysis with the GRF, measurement of multiaxial GRF is important for evaluating forward and lateral motion during natural gait. In this paper, we propose a bendable GRF measurement system that can measure biaxial (vertical and anterior-posterior) GRF without interrupting the natural gait. Eight custom small biaxial force sensors based on an optical sensing mechanism were installed in the proposed system. The interference between two axes on the custom sensor was minimized by the independent application of a cantilever structure for the two axes, and the hysteresis and repeatability of the custom sensor were investigated. After developing the system by the installation of force sensors, we found that the degree of flexibility of the developed system was comparable to that of regular shoes by investigating the forefoot bending stiffness. Finally, we compared vertical GRF (vGRF) and anterior-posterior GRF (apGRF) measured from the developed system and force plate at the same time when the six subjects walked, ran, and jumped on the force plate to evaluate the performance of the GRF measurement system.