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A significant number of stroke patients are permanently left with a hemiparetic upper limb after the poststroke six-month golden recovery period, resulting in a drastic decline in their quality of life. This study develops a novel foot-controlled hand/forearm exoskeleton that enables patients with hemiparetic hands and forearms to restore their voluntary activities of daily living. Patients can accomplish dexterous hand/arm manipulation on their own with the assistance of a foot-controlled hand/forearm exoskeleton by utilizing foot movements on the unaffected side as command signals. The proposed foot-controlled exoskeleton was first tested on a stroke patient with a chronic hemiparetic upper limb. The testing results showed that the forearm exoskeleton can assist the patient in achieving approximately 107° of voluntary forearm rotation with a static control error less than 1.7°, whereas the hand exoskeleton can assist the patient in realizing at least six different voluntary hand gestures with a success rate of 100%. Further experiments involving more patients demonstrated that the foot-controlled hand/forearm exoskeleton can help patients in restoring some of the voluntary activities of daily living with their paretic upper limb, such as picking up food to eat and opening water bottles to drink, and etc. This research implies that the foot-controlled hand/forearm exoskeleton is a viable way to restore the upper limb activities of stroke patients with chronic hemiparesis. [ABSTRACT FROM AUTHOR]

Building a simulated weightless test system on the ground while making comprehensive comparisons of design controllers for a large and heavy multijointed space station robot is not an easy task. To save cost and improve the efficiency of the test, this paper develops a plan in which controllers undergo preliminary testing in a 6-DOF industrial robot. The key idea is gravity compensation included within the dynamic control algorithm of the robot to replace the function of the microgravity environment. It is generally difficult to build an accurate dynamic model for a serial-joint robot in a practical manner. Therefore, to guarantee the stability of the 6-DOF industrial robot in which the dynamic model is built inaccurately, we propose one of the simplest variable structure (VS) controllers, and the stability of the system is analyzed through the Lyapunov method. Last, experiments are carried out to provide preliminary comparisons among three potential algorithms for the space robot in a low-cost and efficient approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rotorcrafts, with satisfactory maneuver performance and ability under complex terrains unreachable for ground robots, are playing important roles for goods transportation. In this article, we focus on the control of the cable-suspended transportation way due to its lower costs and more agility of the rotorcraft’s rotational motion. Compared with traditional crane systems and single rotorcrafts without loads, the aerial transportation system presents “double” underactuated property, stronger system nonlinearity, and more complex dynamic coupling, which are huge challenges for control schemes design. Meanwhile, aerial transportation usually suffers from external disturbances and uncertainties presented with aerodynamic damping coefficients and rope length. Additionally, overshoots of the rotorcraft’s position are potential threats for flight safety, especially in confined and complex environments. To address these problems, a novel adaptive control scheme is designed, which ensures effective rotorcraft positioning and payload swing suppression with restricted overshoot amplitudes. Asymptotic results are obtained with rigorous theoretical derivations provided by the Lyapunov-based stability analysis and LaSalle’s invariance theorem. Real-time experiments are performed to validate the effectiveness of the proposed control scheme even in the presence of external disturbances. To the best of our knowledge, this is the first method designed for aerial transportation systems which achieves simultaneous rotorcraft positioning and swing suppression, together with insurance for overshoot restriction even in the presence of parametric uncertainties. [ABSTRACT FROM AUTHOR]

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 pre-trained 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 sEMG-based rehabilitation systems. [ABSTRACT FROM AUTHOR]

Design of the hollow modular joint plays an important role in modern robot layout, fixation, and wiring. In this paper, a hollow modular joint that meets the requirement of a minimally invasive surgical robot is proposed. The mechanical and control design is sequentially illustrated, and the torque sensor and its optimization are provided. Furthermore, a free-force control method is introduced. To analyze the designed module, the simulation of the redundant robot, comprised of the designed joint in seven degrees of freedom, is presented. The results of analyses showed that the designed hollow modular joint is valid and effective. [ABSTRACT FROM AUTHOR]

In this paper, an active model-based control scheme is developed for the pneumatic artificial muscle (PAM), to compensate for the uncertainties in the dynamics model of the PAM. First, a simplified three-element model with respect to a specific range of pressure is formulated as the reference model. Second, a Kalman filter is adopted to actively estimate the errors involved in the reference model, especially while the PAM was working at the pressure outside the specific range where the reference model was built. Finally, a compensation control is proposed using the estimated errors to reject the modeling errors and improve the performance of the PAM. Experiments are conducted on a one degree-of-freedom testbed actuated by the PAM. The experimental results with and without the active-model-based compensation are presented and compared to demonstrate the improvements of the proposed scheme. [ABSTRACT FROM AUTHOR]

Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia. [ABSTRACT FROM PUBLISHER]