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The integration of electrochromic devices and energy storage systems in wearable electronics is highly desirable yet challenging, because self‐powered electrochromic devices often require an open system design for continuous replenishment of the strong oxidants to enable the coloring/bleaching processes. A self‐powered electrochromic device has been developed with a close configuration by integrating a Zn/MnO2 ionic battery into the Prussian blue (PB)‐based electrochromic system. Zn and MnO2 electrodes, as dual shared electrodes, the former one can reduce the PB electrode to the Prussian white (PW) electrode and serves as the anode in the battery; the latter electrode can oxidize the PW electrode to its initial state and acts as the cathode in the battery. The bleaching/coloring processes are driven by the gradient potential between Zn/PB and PW/MnO2 electrodes. The as‐prepared Zn||PB||MnO2 system demonstrates superior electrochromic performance, including excellent optical contrast (80.6%), fast self‐bleaching/coloring speed (2.0/3.2 s for bleaching/coloring), and long‐term self‐powered electrochromic cycles. An air‐working Zn||PB||MnO2 device is also developed with a 70.3% optical contrast, fast switching speed (2.2/4.8 s for bleaching/coloring), and over 80 self‐bleaching/coloring cycles. Furthermore, the closed nature enables the fabrication of various flexible electrochromic devices, exhibiting great potentials for the next‐generation wearable electrochromic devices. A self‐powered Zn||PB||MnO2 electrochromic device based on a shared electrode design and featuring a closed configuration, has been developed, which consists of a Prussian blue (PB) based electrochromic system and a zinc‐ion battery (ZIB). Both the bleaching and coloring processes are driven by the gradient potentials between Zn/PB and PW/MnO2 electrodes instead of external power supply. Owning to the closed nature of Zn||PB||MnO2 electrochromic system, a range of integrated flexible electrochromic devices are successfully fabricated, including electrochromic glasses, labels, and wristbands, exhibiting great potentials for the next‐generation wearable electrochromic devices.

Triboelectric nanogenerators with the function of harvesting human motion energy have attracted wide attention. Here, we demonstrate a shared-electrode and nested-tube structure triboelectric nanogenerator (SNTN) for harvesting human motion energy. The design of the SNTN employs flexible silicone rubber as the negative friction material and Ni-coated polyester conductive textile as the positive friction material and the electrode material. The entire structure consists of an inner triboelectric unit and an outer triboelectric unit. The inner triboelectric unit is formed by a hollow inner tube and a hollow middle tube, while the hollow middle tube and a hollow outer tube constitute the outer triboelectric unit. The hollow middle tube is used as the shared tube, and the electrode in the middle tube is used as the shared electrode of the two triboelectric units. Our research demonstrates that the output performance of the SNTN was improved significantly compared with a single triboelectric unit due to the cooperation of the two triboelectric units. When the SNTN is pressed by 300 N external force, output open-circuit voltage of 180 V and output short-circuit current of 8.5 μA can be obtained. The output electrical energy can light up 31 light-emitting diodes (LEDs) connected serially (displaying “XZTC”) and can drive a digital clock after rectifying storage, which shows application prospects in the field of illuminating devices and portable electronics.

Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two degrees of freedom (DOF) on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 degrees of freedom for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

Intravascular ultrasound (IVUS) imaging has been extensively utilized to visualize atherosclerotic coronary artery diseases and to guide coronary interventions. To receive ultrasound signals within the vessel wall safely and effectively, miniaturized ultrasound transducers that meet the strict size constraints and have a simple manufacturing procedure are highly demanded. In this work, the first known IVUS probe that employs a backing-layer-shared dual-frequency structure and a single coaxial cable is introduced, featuring a small thickness and easy interconnection procedure. The dual-frequency transducer is designed to have center frequencies of 30 MHz and 80 MHz, and both have an aperture size of 0.5 mm × 0.5 mm. The total thickness of the dual-frequency transducer is less than 700 µm. In vitro phantom imaging and ex vivo porcine coronary artery imaging experiments are conducted. The low-frequency transducer achieves spatial resolutions of 40 µm axially and 321 µm laterally, while the high-frequency transducer exhibits axial and lateral resolutions of 17 µm and 247 µm, respectively. A bandpass filter is utilized to separate the ultrasound images. Combining in vitro phantom imaging analysis with ex vivo imaging validation, a comprehensive demonstration of the promising application of the proposed miniature ultrasound probe is established.

There is extensive evidence that perceived and internally planned actions have a common representational basis: action observation can induce an automatic tendency to imitate others. If perceived and executed action, however, are based on shared representations, the question arises how we can distinguish self-related and other-related representations. It has been suggested that the control of shared representations involves a neural network centered on the temporo-parietal junction (TPJ). However, the specific role of the TPJ in controlling shared representations is still not clear. In a conflict situation where participants have to execute action A while observing action B, the TPJ might either facilitate the relevant action A or inhibit the irrelevant action B (mirror response). In the present study, we used transcranial Direct Current Stimulation (tDCS) to condition neural activity in the right temporo-parietal junction (TPJ). We then analyzed the corticospinal output as indexed by motor-evoked potentials (MEPs) induced by single-pulse TMS (spTMS) of the left primary motor cortex (M1) during action observation in the context of a conflict task. Results showed that tDCS-mediated increased control did not entail the attenuation of the task-irrelevant response activation: the effect of motor mirroring was not suppressed or reduced. Rather, facilitating TPJ activity via anodal tDCS selectively enhanced the instructed motor plan (self-related representation). This outcome supports the idea that TPJ plays a critical role in detecting the mismatch between self-related and other-related representations and is at work to enhance task-relevant representations. •We investigated the functional interaction between TPJ and M1 in the control of shared representations.•TPJ stimulation enhances task-relevant motor representations as indexed by MEPs analysis.

The paper presents an approach to develop assistive devices by combining multimodal biosignals and radio frequency identification (RFID). The brain and eye signals have been used as multimodal biosignals to control the movement of a robot in four directions and help reach near the object following a predefined path. RFID shared control over object identification, and the gripper arm connected at the end effector of the robot performs pick and place operations. Horizontal electrooculography (EOG) has been used for x -directional movement control and electroencephalography (EEG) signal obtained by visual stimulus, called steady-state visual-evoked potential (SSVEP) has been used for y -directional movement control of a robot. The SSVEP signal has also been used to ring an alarm in case of an emergency call by the user. Two parameters classification accuracy (CA) and information transfer rate (ITR) have been calculated for the performance evaluation of the proposed multimodal-shared control model and have shown improved results as compared to previous literature. The results also proved that the proposed model can be used for real-time mobility assistive applications.

Implantable cardioverter defibrillators are implanted on a large scale in patients with heart failure (HF) for the prevention of sudden cardiac death. There are different scenarios in which defibrillator therapy is no longer desired or indicated, and this is occurring increasingly in elderly patients. Usually device therapy is continued until the device has reached battery depletion. At that time, the decision needs to be made to either replace it or to downgrade to a pacing-only device. This decision is dependent on many factors, including the vitality of the patient and his/her preferences, but may also be influenced by changes in recommendations in guidelines. In the last few years, there has been an increased awareness that discussions around these decisions are important and useful. Advanced care planning and shared decision-making have become important and are increasingly recognised as such. In this short review we describe six elderly patients with HF, in whose cases we discussed these issues, and we aim to provide some scientific and ethical rationale for clinical decision-making in this context. Current guidelines advocate the discussion of end-of-life options at the time of device implantation, and physicians should realise that their choices influence patients’ options in this critical phase of their illness.