Reconfigurable intelligent surface (RIS) has been widely envisioned as a key technique, which can enhance communication quality of service (QoS) for mobile users (MUs) by reconfiguring wireless propagation environments. Unlike traditional RISs that can only reflect signals, simultaneously transmitting and reflecting RIS (STAR-RIS) can extend half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. In this paper, we propose the joint optimization for the transmission and reflection coefficients, i.e., phase-shifts and amplitudes, of STAR-RIS and the transmit beamforming of base station (BS) over STAR-RIS aided downlink communications, where MUs move in real time and can change their movement patterns dynamically. First, taking into account MUs' mobility, we formulate a rate maximization problem to maximize the average transmission rate between BS and multiple MUs. Then, since the traditional deep reinforcement learning (DRL) methods struggle to adapt to dynamic network environments, we develop a meta-learning based scheme to optimize STAR-RIS's transmission and reflection coefficients and BS's transmit beamforming. Finally, we validate and evaluate our developed scheme through extensive simulations, showing that our meta-learning based scheme significantly outperforms baseline schemes and can adapt to MUs' new movement patterns quickly.

Hand extension is crucial for stroke survivors with spasticity, where their fingers become rigid and their thumb remains curled within the palm. Due to the underactuated nature of the hand, the dominance of flexor muscles over extensors, and the limited surface area available, developing an extension glove with thumb assistance poses a challenge for researchers. This paper introduces a fully wearable soft hand extension glove based on the X-pouch and strap system, addressing the above challenges. The glove enables adequate finger extension, thumb abduction, and extension for high MAS score patients. Modelling and testing revealed extension torques of up to 2.7 Nm at the MCP joint and 0.67 Nm at the PIP and DIP joints. Performance evaluation, including comparison with existing methods, demonstrated the glove’s superior extension capabilities using a model hand with realistic stiffness. Furthermore, the glove’s effectiveness was confirmed through testing on a stroke patient with MAS = 2, validating its on-body functionality.

Posture sensing of soft actuators is critical for performing closed-loop control of soft robots. This paper presents a novel end-to-end posture perception method for soft actuators by developing long short-term memory (LSTM) neural networks. A novel flexible bending sensor developed from off-the-shelf conductive silicon material was proposed and used for posture sensing. In the proposed method, the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors have also been considered. With one-step calibration from the sensor output, the posture of the soft actuator could be captured by the LSTM network. The method was validated on a finger-size one DOF pneumatic fiber-reinforced bending actuator. Four kirigami-inspired flexible piezoresistive transducers were placed on the top surface of the actuator. Results show that the transducers could sense the posture of the actuator with acceptable accuracy. We believe our work could benefit soft robot dynamic posture perception and closed-loop control.