Accurate acquisition of interactive information is crucial for the effective execution of rehabilitation training. However, due to model and sensor errors, it is difficult to obtain interactive information accurately and quickly. To overcome these challenges, a novel accurate and fast estimation method for the human–robot interaction torques (HRITs) is proposed in this article. First, the HRIT model with order adaptive adjustment ability (HMOAA) is constructed. The polynomial order of HMOAA can be adaptively adjusted based on the partial state estimation, which is more consistent with the dynamic time-varying characteristics of HRIT. Second, the Sage–Husa adaptive strong tracking Kalman filter (SHASTKF) is designed based on the modified Sage–Husa adaptive Kalman filter (SHAKF) and strong tracking Kalman filter (STKF). The SHASTKF can quickly track the abrupt HRIT changes when the subject suddenly exerts active torques in rehabilitation training. Moreover, it also has the ability to recursively estimate the noise characteristics, and can stably complete the HRIT estimation task when the noise characteristics are unknown. Finally, simulations and experiments are conducted to validate the proposed method, and the comparison results demonstrate that the proposed method has good torque estimation precision and fast tracking ability of abrupt changes in HRITs.

Rehabilitation of hand functions is necessary to improve post-stroke patients' quality of life. There is initial evidence that hand exoskeletons should exercise flexion/extension (f/e) and abduction/adduction (a/a) of the fingers to rebuild hand functions. However, designing a self-alignment mechanism of the metacarpophalangeal (MCP) joint to improve its wearing comfort is still a challenge. In this paper, a novel index finger exoskeleton with three motors is proposed to help post-stroke patients perform finger a/a and f/e training. A spatial mechanism with passive degrees of freedom for the MCP joint is designed to realize human-robot axes self-alignment. The proposed mechanism's kinematic compatibility is analyzed to show its self-aligning capability, and the kineto-statics analysis is performed to present the exoskeleton's static characteristics. Finally, kinematic and static experiments have been conducted, and the results indicate that the standardized reaction forces square sum of the exoskeleton to the MCP joint can be reduced by 65.8% compared with the state-of-the-art exoskeleton. According to the experimental results, the exoskeleton can achieve the a/a and f/e training and human-robot axes self-alignment, and improve its comfortability. In the future, clinical trials will be further studied to test the exoskeleton.

Robot-assisted thumb rehabilitation can improve the hand function of stroke patients because the thumb accounts for 40% of hand function. However, existing thumb rehabilitation robots are limited in terms of portability, comfort, adaptability, and independent joint actuation. This article proposes an untethered adaptive thumb exoskeleton that actively assists the 3-degree-of-freedom movements of the thumb. The exoskeleton is composed of an adaptive thumb mechanism and a spherical mechanism. The kinematics, statics, and performances of the adaptive thumb mechanism and the spherical mechanism are analyzed. Experimental validation is performed to test the workspace, self-alignment, interaction forces, admittance controller, and grasping assistance performance of the exoskeleton. The workspace and self-aligning experiments prove that the proposed exoskeleton can achieve a large workspace of the thumb joints and realize self-alignment. The interaction force experimental results show that the exoskeleton can reduce the tangential interaction forces by 76.8% and improve comfort. Finally, the control and delicate grasping assistance experiments validate the exoskeleton's ability to realize the delicate grasping of the thumb. These characteristics show that the thumb exoskeleton has the potential to assist delicate thumb rehabilitation.

The development of robotic hand that can imitate human movements has always been an important research topic. In this paper, a linkage-driven underactuated three-finger hand is proposed to imitate the flexion/extension (f/e) and abduction/adduction (a/a) motions of human hand. The robotic hand has three identical underactuated fingers, each of which contains an underactuated planar linkage, a spherical four-bar mechanism, and a set of bevel gears. The spherical four-bar mechanism is designed to provide 2-degree-of-freedom actuation, driving the f/e and a/a motions of the proximal joint simultaneously. Based on screw theory, the kinematic model of the spherical mechanism is established, and the maximum available workspace index (MAW) of the spherical mechanism is proposed to evaluate the workspace with the same adduction and abduction angle ranges. The effects of the parameters of the spherical mechanism on the MAW and the transmission efficiency are obtained, and the parameters of the spherical mechanism are optimized. The optimization results show that the MAW of the spherical mechanism can be increased by up to 3.5 times. Finally, experiments are carried out to show the proposed robotic hand can perform simultaneous adaptive grasping and in-hand manipulation. Note to Practitioners —The robotic hand is of great significance for replacing workers in heavy, repetitive, and unsafe working environments. Uncertain environments in non-manufacturing fields, such as industries for pick-and-place, sorting, palletizing, assembly and other operations, marine development, and medical service, require that robotic hands can operate tasks like human hands. Inspired by the human hand with the f/e and a/a motions, a linkage-driven underactuated three-finger hand is proposed in this paper to imitate the functions of the human hand. Due to its underactuated characteristics and a/a function, the designed robotic hand is more dexterous and economical. The effects of the design parameters of the spherical mechanism on the MAW and the transmission efficiency are analyzed, and the parameters of the spherical mechanism are optimized to improve the performance of the robotic hand. This approach can be used in other industrial applications related to the robotic hand or the spherical mechanism.

Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adaptation (UDA) method has become an effective way to this problem. However, the labeled data are collected from multiple source subjects that might be different not only from the target subject but also from each other. The current UDA methods for HMI recognition ignore the difference between each source subject, which reduces the classification accuracy. Therefore, this article considers the differences between source subjects and develops a novel theory and algorithm for UDA to recognize HMI, where the margin disparity discrepancy (MDD) is extended to multisource UDA theory and a novel weight-aware-based multisource UDA algorithm (WMDD) is proposed. The source domain weight, which can be adjusted adaptively by the MDD between each source subject and target subject, is incorporated into UDA to measure the differences between source subjects. The developed multisource UDA theory is theoretical and the generalization error on target subject is guaranteed. The theory can be transformed into an optimization problem for UDA, successfully bridging the gap between theory and algorithm. Moreover, a lightweight network is employed to guarantee the real-time of classification and the adversarial learning between feature generator and ensemble classifiers is utilized to further improve the generalization ability. The extensive experiments verify theoretical analysis and show that WMDD outperforms previous UDA methods on HMI recognition tasks.