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.

Soft actuators, made from soft materials, offer a safer alternative to rigid robots for use on hand rehabilitation devices. A current challenge is to ensure these actuators comply with human finger morphology. To gain better insights into actuator mechanics when worn on and interacting with human fingers, combining physical experiments with simulation approaches is necessary. However, no simulation has been implemented for finger-actuator interactions. This letter proposes a new joint modular soft actuator designed to comply with a dummy finger joint. The new actuator has a dual function design for increasing axial elongation during bending, facilitating compliance with finger morphology. In addition, a novel FEM for the new actuator's interaction with the dummy finger joint (dummy joint-soft actuator complex) is developed and used with physical experiments for evaluating actuator performance. Results show that the new design increases the dummy joint's bending range while exerting smaller contact forces on the joint. Even when the joint is blocked at specific bending angles, the actuator remains compliant to finger morphology. This research is a significant advancement in soft actuator design for hand rehabilitation, emphasizing the interaction between human fingers and soft actuators.

Finger joint stiffness assessment is very important in quantifying the degree of disability in stroke patients and determining rehabilitation strategies and plans. However, the evaluation methods used in clinical practice rely heavily on the experience of clinicians. In our previous study, we adapted an analytical model originally proposed for a whole-finger soft actuator to objectively quantify joint stiffness by using a new type of joint modular soft actuator designed for individualized hand rehabilitation. However, stiffness could not be accurately estimated owing to the effects of the interaction between joint modular soft actuators and a finger, as well as, between the actuators themselves, which could not be represented by the adapted analytical model. In this study, artificial neural network (ANN)-based models were proposed to simultaneously quantify the stiffness of three joints using joint modular soft actuators and compared with the adapted analytical model and other machine-learning (ML)-based models. Moreover, the estimation performance was verified for high stiffness values and different finger sizes. The results show that the ANN-based models estimate stiffness more stably and accurately than the adapted analytical and other ML-based models. This study shows the feasibility of quantitative evaluation of joint stiffness using joint modular soft actuators.

Accurate estimation of finger joint stiffness is important in assessing the hand condition of stroke patients and developing effective rehabilitation plans. Recent technological advances have enabled the efficient performance of hand therapy and assessment by estimating joint stiffness using soft actuators. While joint modular soft actuators have enabled cost-effective and personalized stiffness estimation, existing approaches face limitations. A corrective approach based on an analytical model suffers from actuator–finger and inter-actuator interactions, particularly in multi-joint systems. In contrast, a data-driven approach struggles with generalization due to limited availability of labeled data. In this study, we proposed a method for energy conservation-based online tuning of the analytical model using an artificial neural network (ANN) to address these challenges. By analyzing each term in the analytical model, we identified causes of estimation error and introduced correction parameters that satisfy energy balance within the actuator–finger complex. The ANN enhances the analytical model’s adaptability to measurement data, thereby improving estimation accuracy. The results show that our method outperforms the conventional corrective approach and exhibits better generalization potential than the purely data-driven approach. In addition, the method also proved effective in estimating stiffness in human subjects, where errors tend to be larger than in prototype experiments. This study is an essential step toward the realization of personalized rehabilitation.

Assisting patients with weakened hand and wrist strength during meals is essential. While various feeding devices have been developed, many do not utilize patients’ residual finger functions, leading to an increase in the risk of disuse syndrome and loss of joy in life. Recently, assist-as-needed support for spoon grasping by soft hand rehabilitation devices has been studied. Moreover, in our previous study, we investigated finger motions for the required scooping angle and verified them with a dummy hand driven by soft actuators. However, eating with a spoon requires not only spoon grasping and rotating but also plunging the spoon into food and lifting it afterward. The goal of this study is to achieve self-feeding with spoons using soft actuators for individuals with partial finger disabilities. To address this, we measured scooping movements using inertial measurement units, identified feasible finger motions for spoon plunging and lifting, and verified our findings through experiments with a dummy hand driven by soft actuators. As a result, we found a way to achieve the two motions by regulating the moment applied to the spoon. These results highlight the potential of soft actuators for assisting scooping movements. This study marks an important step toward feeding assistance that leverages patients’ residual finger functions.

Soft actuators are safer than rigid robots for hand rehabilitation, yet their performance can be significantly affected by interactions that occur on multi-joint systems. Actuator–finger and actuator–actuator interactions can impact bending output and make actuator performance dependent on the actuation pattern. To address this, we developed and validated a finite element model of a three-joint modular actuator system attached to a dummy finger. The simulation revealed that the displacement between actuators, and the contact area between the fingers and the actuators are key factors influencing actuator performance. We proposed a novel attachment method to enhance contact area and reduce actuator displacement and compared it against five existing designs across two actuator types and three actuation patterns. Our results demonstrate improved bending and reduced dependence of actuator performance on actuation pattern. This study makes a dual contribution to the area of soft robotics for hand rehabilitation by proposing a novel FEM framework for modeling soft actuators attached to multi-joint systems as well as providing insights on attachment method design for soft actuators in hand rehabilitation, emphasizing the importance of actuator–actuator and actuator–finger interactions.