Time-dependent shape-transferable soft robots are important for various intelligent applications in flexible electronics and bionics. Four-dimensional (4D) shape changes can offer versatile functional advantages during operations to soft robots that respond to external environmental stimuli, including heat, pH, light, electric, or pneumatic triggers. This review investigates the current advances in multiscale soft robots that can display 4D shape transformations. This review first focuses on material selection to demonstrate 4D origami-driven shape transformations. Second, this review investigates versatile fabrication strategies to form the 4D mechanical structures of soft robots. Third, this review surveys the folding, rolling, bending, and wrinkling mechanisms of soft robots during operation. Fourth, this review highlights the diverse applications of 4D origami-driven soft robots in actuators, sensors, and bionics. Finally, perspectives on future directions and challenges in the development of intelligent soft robots in real operational environments are discussed.

For active AFO applications, pneumatic remote transmission has advantages in minimizing the mass and complexity of the system due to the flexibility in placing pneumatic components and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors, which greatly limit the practical daily usage. In this study, we implemented a wearable custom compressor that can be worn at the trunk of the body and can generate up to 1050 kPa of pressurized air to power an unilateral active AFO for dorsiflexion (DF) assistance of drop-foot patients. In order to minimize the size and weight of the custom compressor, the compression rate of the custom compressor was optimized to the rate of consumption required to power the active AFO. The finalized system can provide a maximum assistive torque of 9.8 Nm at a functional frequency of 1 Hz and the average resistive torque during free movement was 0.03 Nm. The system was tested for five hemiplegic drop-foot patients. The proposed system showed an average improvement of 12.3° of ankle peak dorsiflexion angle during the mid to late swing phase.

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.

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.

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.