In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about 0:017 0:13 radians. Eight gestures recognition results have shown an average accuracy around 79:32%, which represents a significant improvement over the 35:72% (p < 0:0001) average accuracy of results obtained using nonadaptive models, and 60:99% (p < 0:0001) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pretrained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects re-training burden of sEMGbased rehabilitation systems.

When applying exoskeletons for walking assistance, one important consideration is to ensure that the users retain full control over the exoskeleton-provided assistance, which is quite limited in existing exoskeletons due to the absence of a suitable control system. In this article, a time-independent exoskeleton control system is developed based on a novel assistance profile generation method and an iterative force control method to enable continuous assistance adjustment. The assistance profile is formulated as a Gaussian function with a human state variable and can be updated online to adapt to different users. The proposed profile continuously self-adjusts along the movement of the user's leg, especially when users change their walking patterns. The proposed control system iteratively compensates for the force control lag and amplitude attenuation to enable precise tracking of the assistance profile during natural human walking. Experiments have been conducted using a soft exoskeleton on subjects with and without prior experience using an exoskeleton. The experimental results have shown the effectiveness of the proposed control system compared with a common time-dependent control system.

Soft exoskeletons have demonstrated the potential to save energy, but their efficiency is sensitive to variations in human gait cadence. This work aims to develop adaptive gait state detection and iterative force control methods for a soft exoskeleton to reduce human walking metabolic cost consistently, while the user may change walking cadence. The proposed approach is motivated by the rhythmicity of gait and applies an iterative learning concept to enhance the exoskeleton’s adaptability to varying walking conditions. The gait state detection method proposed for the designed exoskeleton combines two feature extraction algorithms, which can learn from the present and past body kinematic data, to provide accurate user gait state detection. Based on the state, the proposed force control method iteratively adjusts the commands to keep track of the desired profile. Experiments have been conducted on healthy subjects walking with varying cadence using the soft exoskeleton. Promising results were presented in separate validation tests. Moreover, metabolic costs of subjects walking under one unpowered and two powered conditions, where the assistance profiles were produced by classical methods and the proposed methods, showed that the proposed methods can effectively improve the exoskeleton’s ability to save human energy of walking with varying cadence. Note to Practitioners —Lower limb exoskeletons have demonstrated the potential to save human energy in medical and industrial applications. The main purpose of this work is to solve the exoskeleton assistance efficiency loss problem for users walking with changing cadence. Constant cadence is unlikely maintained during natural human walking. Few existing exoskeletons could retain high efficiency under user cadence changes, limited by their control system capability. This work presents a new cable-driven cadence-insensitive soft exoskeleton, which is purposely designed with two adaptive methods to enable the device to offer consistent benefit to users walking with varying cadence. The proposed methods are inspired by the rhythmicity of human gait and can be iteratively reconfigured to perform accurate human gait state detection and assistive force tracking. The proposed methods have the potential to be integrated into other human-oriented robots to improve their adaptability. This work can greatly enhance the possibility of using the walking assist robotic devices in more practical applications.

In this paper, an active modeling and control scheme is developed for Shape Memory Alloy (SMA) actuators to eliminate the negative influences caused by the uncertainties in its dynamics. First, a nonlinear SMA dynamic model based on Liang model and the empirical models is built and linearized, and all the uncertainties due to time-varying parameters, external disturbances, as well as the linearization, are considered as model error of the linearized model. Secondly, an active modeling based on Kalman filter is constructed to estimate the model error in real time, which intends to improve the model accuracy actively. Finally, an active modeling based control method is proposed to compensate the model error in order to improve control performance of SMA actuators. Experiments are conducted on a one degree-of-freedom (DOF) testbed actuated by a SMA wire. The experimental results of the active model error estimation, and the control performance with and without the active model based compensation are presented and compared to demonstrate the improvements of the proposed scheme.

While rehabilitation exoskeletons have been extensively studied, systematic design principles for effectively addressing heterogeneous bilateral locomotion in hemiplegia patients are poorly understood. In this article, a multijoint lower exoskeleton driven by series elastic actuators (SEAs) is developed, and the design philosophy of rehabilitation robots for hemiplegia patients is systematically explored. The exoskeleton has six powered joints for both lower limbs in a hip–knee–ankle configuration, and each joint incorporates a custom, lightweight SEA module. A unified interaction-oriented control framework is designed for exoskeleton-assisted walking, including gait generation, task scheduling, and advanced joint-level control. The closed-loop design provides methodical solutions to address hemiplegia rehabilitation needs and provides walking assistance for bilateral lower limbs. Moreover, a multitemplate gait generation approach is proposed to address the altered kinematics induced by exoskeleton-assisted walking and enhance the exoskeleton's adaptability to patient-specific kinematic variations in an iterative manner. Experiments are conducted with both healthy individuals and hemiplegia patients to verify the effectiveness of the exoskeleton system. The clinical outcomes demonstrate that the exoskeleton can achieve mechanical transparency, facilitate movement, and enable coordinated interjoint locomotion for bilateral gait assistance.

With coexisting-cooperative-cognitive robots gradually appearing in daily life, an instinct and efficient human-robot interaction (HRI) is becoming more and more challenging and necessary. Surface electromyography (sEMG) signals, as one of mainstream manners of the interactions, are employed to predict human intentions. In this paper, to provide natural assistance for standing up and sitting down, sEMG signals acquired from active muscles of one’s lower limb are utilized to predict continuous movements. A temporally smoothed multilayer perceptron (MLP) regression scheme is proposed for continuous knee/ankle angles estimation by multi-channel sEMG signals. After correlation analyses of sEMG signals and movements, a series of linear and nonlinear regression models are trained to decode human intentions from pre-processed sEMG. Furthermore, to remove out local fluctuations of direct mappings, temporally smoothed techniques are further implemented as post-processings. In the experiments of standing up and sitting down, extensive results of ten healthy subjects show that a three-layer MLP with the Savitzky-Golay filter achieves the best performance on the mean squared error (MSE, testing: 59.58) and the R2 score (R2, testing: 0.948). The proposed regression scheme is compared with other methods and is also verified by measurements of a high-precision visual motion capture system.