Studies have developed various types of soft robotic gloves for hand rehabilitation in recent years. Most soft actuators achieved a sufficient thumb flexion assist while lacking opposition support, which requires the coordination of thumb flexion and abduction-adduction. The difficulties for thumb support lie in the intrinsic complexity of thumb movements and spatial restriction of the hand. To realize multiple degrees of freedom of the thumb and make effective use of the limited space of the hand's dorsal side, we optimized and compared two approaches for thumb support. The combination approach used two independent soft actuators for thumb flexion and abduction-adduction support, respectively. The all-in-one approach used one single soft actuator to assist motions in different directions. We designed the soft actuators for each approach based on the thumb's biomechanical characteristics and evaluated their thumb flexion, abduction support performance in terms of the range of motion (RoM) and force output, and the opposition support performance using an enhanced Kapandji test. The results showed a larger abduction RoM and force output of the composition approach and a higher Kapandji score of the all-in-one approach, indicating that the two approaches might be applicable for thumb support but have the advantage in different rehabilitation stages.

Stroke survivors might lose their walking and balancing abilities, but many studies pointed out that cycling is an effective means for lower limb rehabilitation. However, during cycle training, the unaffected limb tends to compensate for the affected one, which resulted in suboptimal rehabilitation. To address this issue, we present a Virtual Reality-Cycling Training System (VRCTS), which senses the cycling force and speed in real-time, analyzes the acquired data to produce feedback to patients with a controllable VR car in a VR rehabilitation program, and thus specifically trains the affected side. The aim of the study was to verify the functionality of the VRCTS and to verify the results from the ten stroke patients participants and to compare the result of Asymmetry Ratio Index (ARI) between the experimental group and the control group, after their training, by using the bilateral pedal force and force plate to determine any training effect. The results showed that after the VRCTS training in bilateral pedal force it had improved by 0.22 (p=0.046) and in force plate the stand balance has also improved by 0.29 (p=0.031); thus both methods show the significant difference.

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.

Electromyogram (EMG) signals have attracted much attention and have been applied widely. EMG signals have been used to evaluate the functional status of skeletal muscles. The commercial EMG acquisition systems are costly, so many researchers have provided low-cost solutions. Although the wireless acquisition systems are realized, the transmission speed is not sufficient for high-sampling and multichannel measurement. The purpose of this work is to implement a low-cost and wireless surface EMG acquisition system supporting highsampling and multi-channel measurement. Using wearable EMG sensors combined with a microcontroller unit and a Wi-Fi module, the measured surface EMG signal samples can be forwarded immediately to the host for further processing and additional applications. Besides, the proposed system was validated by applying the commercial EMG detection system. The results show that all the correlation coefficients are over 0.8 that represent moderate to high relative agreement between the commercial and the proposed system.

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.