Applying soft actuators to hand motion assist for rehabilitation has been receiving increasing interest in recent years. Pioneering research efforts have shown the feasibility of soft rehabilitation gloves (SRGs). However, one important and practical issue, the effects of users’ individual differences in finger size and joint stiffness on both bending performance (e.g., Range of motion (ROM) and torque) and the mechanical loads applied to finger joints when the actuators are placed on a patient’s hand, has not been well investigated. Moreover, the design considerations of SRGs for individual users, considering individual differences, have not been addressed. These, along with the inherent safety of soft actuators, should be investigated carefully before the practical use of SRGs. This work aimed to clarify the effects of individual differences on the actuator’s performance through a series of experiments using dummy fingers designed with individualized parameters. Two types of fiber-reinforced soft actuators, the modular type for assisting each joint and conventional (whole-finger assist) type, were designed and compared. It was found that the modular soft actuators respond better to individual differences set in the experiment and exhibit a superior performance to the conventional ones. By suitable connectors and air pressure, the modular soft actuators could cope with the individual differences with minimal effort. The effects of the individualized parameters are discussed, and design considerations are extracted and summarized. This study will play an important role in pushing forward the SRGs to real rehabilitation practice.

In a telerehabilitation environment, it is difficult for a therapist to understand the condition of a patient’s finger joints because of the lack of direct assessment. In particular, not enabling the provision of spasticity evaluation significantly reduces the optimal performance of telerehabilitation. In a previous study, it has been proposed that finger stiffness could be estimated using an analytical model of a whole-finger soft actuator. However, because the whole-finger soft actuators require high air pressure for high bending performance and are costly to customize for each patient, using joint modular soft actuators for telerehabilitation turns to be a necessity, though stiffness estimation with joint modular soft actuators has not been studied yet. Another problem is caused by using a marker-based joint angle measurement, which requires the markers to be attached to the exact positions, and limits its application in telerehabilitation. In this study, we proposed a procedure of finger joint stiffness estimation that combines information acquired from a joint modular soft actuator and a marker-less hand joint position acquisition device. Correction parameters were added to the previous analytical model for -the bending analysis of a joint assisted using a joint modular soft actuator. Moreover, a multi-variate regression model was implemented for correcting joint angles obtained from the hand joint position acquisition device. As a result, a reasonable accuracy of stiffness estimation was achieved for rehabilitation with the joint modular soft actuators, which suggests the possibility of using the proposed method to evaluate the finger spasticity in a telerehabilitation environment. This is a big step forward towards optimal hand telerehabilitation.

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller’s effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life.

Soft actuators developed for hand rehabilitation show promise, but their practical application requires addressing individual differences and establishing suitable design rules. While joint modular soft actuators offer flexibility for diverse hand dimensions, existing performance validations have only examined a limited number of actuator sizes and neglected crucial factors like joint alignment. Customization efforts have lacked standardization, relying on a trial-and-error approach. Therefore, this study systematically evaluates the impact of actuator design parameters (size and mounting position) on joint range of motion (ROM) and torque, proposing novel design rules based on linear optimization. Experimental assessments, conducted on dummy fingers emulating human biomechanics, provide profound insights into the intricate interplay of design parameters and support performance. The findings strongly advocate maintaining a reference position for stable support, irrespective of actuator size, emphasizing the need to align the actuator with the joint center. The proposed design rules incorporate user-specific finger information, offering customized design rules. Proportionate actuator lengths to single-phalangeal length (PsPL), Proportionate actuator lengths to multi-phalangeal length (PmPL), Proportionate actuator lengths to Range of Motion (PROM), and the Traditional Method are systematically compared. Assistive performances (ROM and torque) and trajectory analysis reveal that the PsPL-designed actuator exhibits the most stable and natural assistive performance. This study emphasizes the importance of understanding actuator deformations for personalized adaptation, providing valuable insights for advancements in assistive and rehabilitative technologies.

Pneumatic artificial muscles (PAMs) have been widely applied to robotic systems, especially assistive devices, which could benefit from PAMs’ intrinsic viscoelasticity. However, nonlinearity and hysteresis make it challenging to achieve high-accuracy control. Using pre-tensioned springs to replace one side PAMs in an antagonistic structure is one efficient way to improve control accuracy. However, in the previous study, the selection method for spring, especially the range of spring constant has not been sufficiently investigated. This study designed a method to find the optimal spring constant based on gradient descent algorithm; and verified it with prototype and simulation experiments. Results showed that the group with optimal spring constant leads to a faster response time, smaller overshoot, and lower steady-state error, which indicated that the proposed method could find optimal spring constant. Moreover, the sensitivity of PAMs dynamic model parameters to the optimal spring stiffness was investigated. This study provides insights to practical use of soft actuators.