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Abnormal muscle synergies during sit-to-stand (STS) transitions have been observed post-stroke, which are associated with deteriorated lower-limb function and mobility. Although exoskeletons have been used in restoring lower-limb function, their effects on muscle synergies and lower-limb motor recovery remain unclear. Here, we characterized normal muscle synergy patterns during STS activity in ten healthy adults as a reference, comparing with pathological muscle synergy patterns in ten participants with subacute stroke. Moreover, we assessed the effects of a 3-week exoskeleton-assisted STS training intervention on muscle synergies and clinical scores in seven stroke survivors. We also investigated correlations between neuromuscular complexity of muscle synergies and clinical scores. Our results showed that the STS task involved three motor modules representing distinct biomechanical functions among healthy subjects. In contrast, stroke participants showed 3 abnormal modules for the paretic leg and 2 modules for the non-paretic leg. After the intervention, muscle synergies partially shifted towards the normal pattern observed in healthy subjects on the paretic side. On the non-paretic side, the synergy modules increased to three and neuromuscular coordination improved. Furthermore, the significant intervention-induced increases in Fugl-Meyer Assessment of Lower Extremity and Berg Balance Scale scores were associated with improved muscle synergies on the non-paretic side. These results indicate that the paretic side demonstrates abnormal changes in muscle synergies post-stroke, while the non-paretic side can synergistically adapt to post-stroke biomechanical deviations. Our data show that exoskeleton-based training improved lower-limb function post-stroke by inducing modifications in muscle synergies.

How to generate anthropomorphic reaching movement remains a challenging problem in service robots and human motor function repair/reconstruction equipment. However, there is no universally accepted computational model in the literature for reproducing the motion of the human upper limb. In response to the problem, this article presents a computational framework for generating reaching movement endowed with human motion characteristics that imitated the mechanism in the control and realization of human upper limb motions. This article first establishes the experimental paradigm of human upper limb functional movements and proposes the characterization of human upper limb movement characteristics and feature movement clustering methods in the joint space. Then, according to the specific task requirements of the upper limb, combined with the human sensorimotor model, the estimation method of the human upper limb natural postures was established. Next, a continuous task parametric model matching the characteristic motion class is established by using the Gaussian mixture regression method. The anthropomorphic motion generation method with the characteristics of the smooth trajectory and the ability of natural obstacle avoidance is proposed. Finally, the anthropomorphic motion generation method proposed in this article is verified by a human-like robot. The measurement index of the human-likeness degree of the trajectory is given. The experimental results show that for all four tested tasks, the human-likeness degrees were greater than 90.8%, and the trajectories’ jerk generated by this method is very similar to the trajectories’ jerk of humans, which validates the proposed method. [ABSTRACT FROM AUTHOR]

How to use few actuators to endow a multifingered robotic hand with the human grasping and manipulation abilities is a challenging problem. This article develops a design principle of a five-fingered robotic hand with only two actuators, which guarantees the designed hand can replicate a large portion of the grasping and a limited portion of the manipulation abilities of the human hand: simultaneously move all joints before contacting objects like the natural reach-to-grasp process of the human hand; self-adaptively pinch/envelop objects with arbitrary shapes; and carry out dexterous manipulation, such as in-hand manipulation of a ball and screwing bottle cap. The core design ideas involve two levels. The higher level idea is to construct a robotic design theory ensuring thumb and fingers driven independently and four fingers driven synergistically so that the opposable thumb with manual abduction/adduction function can move coordinately with four fingers to ensure abundant anthropomorphic grasps and in-hand manipulations. The lower level idea is to establish a mechanism design method that embeds human two primary eigengrasps into a novel single-actuated differential palm mechanism consisting of gears, drag axes, and slider elements, so that the mechanism can always replicate the grasping behaviors of human hand, no matter how the differential processes it would perform during grasping. Finally, we design a mini X-hand to verify our proposed design principle and reproduce 29 types out of 33 grasping types and some manipulations that are commonly used by human hands in daily life. [ABSTRACT FROM AUTHOR]

A high-integration and low-cost transmitter packaging solution for 0.2-THz system-in-package (SiP) application is newly presented using high-temperature co-fired ceramic (HTCC) technology. To improve 3-D integration, internal vertical air-filled interconnection waveguides buried in the HTCC package are explored and fabricated. A compact MoCu waveguide bandpass filter (BPF) is designed and integrated into the package to suppress the harmonic output of the mixer. To demonstrate the potential SiP application in the future 0.2-THz radar systems, based on the proposed solution, two 0.2-THz transmitters using frequency multipliers/mixer are designed, fabricated, and measured. All the components are buried in HTCC, and the overall size is $36\times 14\times9.8$ mm3 and $54\times 14.5\times9.8$ mm3, respectively. Both the transmitters show good performance, and the measured output power is better than 8 and 5 dBm, respectively. [ABSTRACT FROM AUTHOR]

Upper limb exoskeletons have drawn significant attention in neurorehabilitation because of the anthropomorphic mechanical structure analogous to human anatomy. Whereas, the training movements are typically unorganized because most exoskeletons ignore the natural movement characteristic of human upper limbs, particularly inter-joint postural synergy. This paper introduces a newly developed exoskeleton (Armule) for upper limb rehabilitation with a postural synergy design concept, which can reproduce activities of daily living (ADL) motion with the characteristics of human natural movements. The semitransparent active control strategy with the interactive force guidance and visual feedback ensured the active participation of users. Eight participants with hemiplegia due to a first-ever, unilateral stroke were recruited and included. They participated in exoskeleton therapy sessions for 4 weeks, with passive/active training under trajectories and postures with the characteristics of human natural movements. The primary outcome was the Fugl-Meyer Assessment for Upper Extremities (FMA-UE). The secondary outcomes were the Action Research Arm Test(ARAT), modified Barthel Index (mBI), and metric measured with the exoskeleton After the 4-weeks intervention, all subjects showed significant improvements in the following clinical measures: the FMA-UE (difference, 11.50 points, p = 0.002), the ARAT (difference, 7.75 points ${p} < 0.001$), and the mBI (difference, 17.50 points, ${p} = 0.003$) score. Besides, all subjects showed significant improvements in kinematic and interaction force metrics measured with the exoskeleton. These preliminary results demonstrate that the Armule exoskeleton could improve individuals’ motor control and ADL function after stroke, which might be associated with kinematic and interaction force optimization and postural synergy modification during functional tasks. [ABSTRACT FROM AUTHOR]

This article designed a soft bending actuator (SBA) and presented a neural-network-based tracking control strategy for such an actuator. To achieve high-precision control, a visual feedback system was established by using a high-speed camera to dynamically compute the corresponding central angle which described the curvature of the SBA. Considering its nonlinear features, the echo state Gaussian process, a fusion of echo state network and Gaussian process, was used to approximate the SBA's dynamics with a nonlinear autoregressive exogenous model. On this basis, a single-layer neural network was trained to calculate the control signal in light of the idea of predictive control, due to its simplicity and good approximation capabilities. The stability of the closed-loop system was guaranteed, and experimental results indicated that the proposed strategy showed relatively high tracking accuracy under various reference trajectories. [ABSTRACT FROM AUTHOR]

In pneumatic muscle actuators (PMAs)-driven robotic applications, there might exist unpredictable shocks which lead to the sudden change of desired trajectories and large tracking errors. This is dangerous for physical systems. In this article, we propose a novel adaptive proxy-based robust controller (APRC) for PMAs, which is effective in realizing a damped response and regulating the behaviors of the PMA via a virtual proxy. Moreover, the integration of the APRC and the nonlinear disturbance observer further handles the system uncertainties/disturbances and improves the system robustness. According to the Lyapunov theorem, the tracking states of the closed-loop PMA control system are proven to be globally uniformly ultimately bounded through two motion phases. Extensive experiments are conducted to verify the superior performance of our approach, in multiple tracking scenarios. [ABSTRACT FROM AUTHOR]