Displaced pelvic fractures present real surgical challenges because of complex three-dimensional deformity patterns and proximity to vital structures, with conventional manual reduction techniques limited by accuracy constraints and radiation exposure. Although robotic assistance shows promise in preclinical studies, its clinical effectiveness remains unproven in randomized clinical trials (RCTs). (1) Does robotic closed reduction improve reduction quality compared with manual closed reduction in displaced pelvic fractures? (2) Can robotic closed reduction reduce intraoperative radiation exposure while maintaining functional outcomes? In this multicenter RCT conducted at six tertiary trauma centers in China involving 10 senior orthopaedic traumatologists, 92 adult patients with acute closed, displaced pelvic fractures (Tile Type B or C) were randomized 1:1 to robotic closed reduction (n = 46) or manual closed reduction (n = 46) groups. At 12 weeks, loss to follow-up for patient-reported outcomes was 9% (4 of 46) in the robotic group and 4% (2 of 46) in the manual group; the remainder were handled in a prespecified per-protocol analysis. In the robot group, reduction was planned using CT-based three-dimensional reconstruction with contralateral pelvic symmetry as the target and executed by a robotic arm with adjunct elastic traction and contralateral pelvic stabilization. In the manual group, reduction was performed using traction and manual manipulation under fluoroscopic guidance. Surgeons and patients were not blinded; radiographic outcome assessors and data analysts were blinded. Primary outcome was reduction quality assessed using Matta criteria (excellent ≤ 4 mm residual displacement, good 5 to 10 mm, acceptable 10 to 20 mm, poor > 20 mm), analyzed as the proportion of excellent to good reductions. Secondary outcomes included intraoperative surgeon fluoroscopic exposure and 12-week Majeed pelvic scores (0 to 100 points across seven domains; higher scores indicate better function). The primary analysis was intention to treat. In the intention-to-treat analysis, a higher proportion of patients who underwent robotic closed reduction achieved an excellent or good reduction than did those who received manual closed reduction (96% [44 of 46] versus 48% [22 of 46], relative risk 2.00 [95% confidence interval (CI) 1.47 to 2.72]; p < 0.001). Median (IQR) intraoperative surgeon fluoroscopic exposure was lower in the robotic closed reduction group (0 [0 to 0] versus 38 [14 to 78] fluoroscopic exposures; p < 0.001). No differences were found in 12-week Majeed functional scores between groups (mean ± SD 69 ± 16 versus 71 ± 17, mean difference -3 [95% CI -11 to 6]; p = 0.55). One superficial infection occurred in the manual closed reduction group, and there were no serious complications in either group. Surgeons treating acute displaced pelvic ring fractures should consider robotic closed reduction, when available, to improve reduction quality and reduce intraoperative fluoroscopic exposure, although it did not result in improved patient-reported outcome scores at short term in this randomized trial. Future studies should evaluate longer term functional benefits, define the fracture patterns most likely to benefit, and evaluate implementation factors including learning curve and cost-effectiveness across varied trauma settings. Level I, therapeutic study.

With the recent upgrades of synchrotron facilities and an increasing demand for artificial intelligence (AI)-ready scientific datasets, there is a growing interest in high-throughput and automated experimental workflows to accelerate large-scale data-driven research. In this work, we demonstrate a fully automated data collection workflow for synchrotron micro-computed tomography experiment on soil cores on beamline 7-BM at the Advanced Photon Source (APS), operating without expert supervision. The automation integrates sample exchange, multi-position movement and data acquisition. A self-contained robotic arm system is designed for rapid and reliable sample exchange. It accommodates various sample types and can be readily deployed across multiple APS beamlines. Additionally, the beamline has recently upgraded the sample stage stacks to enable a large travel range and an efficient macroscope-based imaging system, enabling characterization of highly attenuating and large samples. This work establishes a foundation for future closed-loop, adaptive and intelligent synchrotron micro-computed tomography experiments.

Automatic imitation towards robots is not influenced by the intentional stance.

Damage to the corticospinal tract after stroke and spinal cord injury (SCI) often results in persistent upper extremity (UE) impairment. Transcutaneous spinal stimulation (TSS) and robotic technologies have been explored as approaches to facilitate motor training; however, their combined effects on UE sensorimotor recovery remain poorly understood. The purpose of this study was to examine the effects of TSS combined with UE robotic training in individuals with chronic stroke or SCI. Five participants with stroke and six with SCI completed a 14-week, sham controlled, single blind crossover study consisting of four total weeks of assessments (one week each pre and post for both training phases), four weeks of UE training with sham TSS, a two-week washout period, and four weeks of UE training with active TSS. Each one-hour session (three days/week) included robotic exoskeleton-assisted UE movements and hand grip training, performed concurrently with sham or active TSS. Assessments included electrophysiological measurements and standardized rehabilitation outcomes. Descriptive analysis revealed meaningful individual improvements masked by group-level heterogeneity. In the stroke group, three participants showed grip strength improvement (assessed without stimulation) after the active phase (+ 9.4 Newtons [N] to + 23.9N), with two-to-four-fold increases in forearm muscle activation. Mean Fugl-Meyer overall UE scores improved from 89 to 94.2. In the SCI group, two participants showed grip strength gains. One participant exhibited a six-fold immediate force increase (1.0N to 6.2N) during stimulation. Another participant achieved improved grip strength without stimulation (23.9N to 36.8N) and a three-fold increase in electromyography (EMG) activity from the flexor carpi radialis and first dorsal interosseous muscles, alongside partial pin-prick sensory recovery and self-reported restoration of previously affected perspiration during the active TSS phase. Varied outcomes in participants confirm that therapeutic effects of combined TSS and robotic UE training are highly individualized. Three critical elements must be blended for the best outcomes of this combinatorial approach: residual UE function, a curated stimulation paradigm, and tailored UE training that provides appropriate challenge, intensity, and salience. The results suggest TSS with UE robotic training hold key potential when considered in the context of the physiological and functional profile of each participant.

Humanoid robots can be seamlessly integrated into human-robot interaction scenarios due to their human-like appearances. Pneumatic artificial muscles (PAMs) are promising actuators for such robots due to their similarity to biological muscles, but their limited contraction ratio constrains both appearance and motion range of the robot. This work presents a 7-degrees-of-freedom (DoF) pneumatic humanoid robotic arm that mimics the human arm in both appearance and movement capabilities. A hybrid actuation scheme, combining direct PAM actuation at shoulder joints and PAM actuation with Bowden cable transmission at the distal joints, is adopted to enable anthropomorphic scaling with a lightweight and compliant structure. To address the control challenges posed by the nonlinear dynamics of PAMs and Bowden cables, pneumatic Bowden cable optimized soft actor-critic (PBO-SAC), a model-free reinforcement learning framework, is proposed to enable efficient on-hardware control policy learning for the robotic arm. PBO-SAC incorporates posture-perturbed decoupled training and local recurrent fusion networks to ensure safe and smooth policy learning. Simulation results verify improvements in PBO-SAC, while hardware experiments on trajectory tracking and teleoperated stacking tasks further demonstrate the multi-DoF coordination control performance.

This paper addresses stable grasping and precise manipulation of objects with unknown parameters under continuous contact, proposing a dynamic modelling and adaptive control framework for soft-fingertip robotic hands. To resolve the singularity of the system inertia matrix caused by compliant contact, the Extended Rosenberg Embedding Method is adopted. This approach systematically decomposes the mass matrix into nonsingular lower-dimensional block matrices, thereby circumventing the strict requirement of a full-rank global matrix. Simultaneously, joint angle limits are embedded as equality constraints via a diffeomorphic transformation to ensure feasible actuator solutions. In controller design, a compensation term combining an improved Extreme Learning Machine network with an adaptive weight-update law is designed to counteract the influence of system parametric uncertainties on stability. This compensation term, together with a feedforward term derived from the dynamic model and servo constraints and a feedback term based on tracking errors, collectively form a composite control law. Theoretical analysis demonstrates that under this control law, the tracking error for object manipulation is uniformly ultimately bounded. Numerical simulations further verify the effectiveness of the framework: in the presence of uncertain object parameters, the tracking error achieves asymptotic convergence within finite time.

Purpose This study develops a novel nursing bed toilet system integrated with a soft robotic hand and an infra-red array camera sensor to assist older adults’ post-defaecation hygiene, while reducing caregiver workload and enhancing patient comfort and dignity. Materials and Methods The novel nursing bed toilet was designed through an integrated mechanical, control, and sensing framework. The system comprises a robot arm, a Soft Robotic hand, and an infra-red array sensor. The soft robotic hand integrates three units: an ultrasonic vibration cleaning, a water spray and a tendon-driven actuation mechanism for post-defaecation perianal hygiene. To clean the buttocks, it is necessary to know the position of the anus. Therefore, the sensing system was developed to determine the position of the anus using a thermal infra-red sensor. Due to the sensitivity of the area, a heating circuit was created to simulate the human anus temperature for system validation. The anus temperature was measured in 17 male participants using a digital thermometer. The average temperature was 37.1 °C. Experimental evaluations included static and dynamic positioning tests using a plastic buttock model with a heating circuit simulating the human anus, supporting the reliability of thermal-based detection. Results Experimental evaluation of the toilet achieved high accuracy in anus positioning with a maximum error of 5 mm and a stable dynamic response, demonstrating reliable localisation performance. Conclusion This work presents the first full implementation of a toilet. It enables autonomous perianal hygiene for patients in nursing beds, reducing caregiver burden while preserving patient comfort and dignity.

The growing prevalence of age-related limb loss underscores the need for prosthetic technologies that restore not only motor function but also authentic sensory feedback. Current prosthetic systems largely depend on sensory substitution or signal remapping, which fall short of replicating natural somatosensory signals. In this work, we develop a plant-enhanced bionic mechanoreceptor that mimics biological touch by converting mechanical stimuli into ionic signals. Incorporating bio-derived pollen microgels into the hydrogel matrix introduces interfacial ion-anchoring sites that strengthen cation-matrix interactions, enhance ionic polarization, and significantly amplify the piezoionic output. This enhancement arises from pressure-driven asymmetric ion transport within the ionically conductive hydrogel. As a result, the output signal increases by up to 12-fold, providing a simple and accessible strategy to improve the sensitivity of piezoionic mechanoreceptors. Then, we demonstrate the integration of ten such mechanoreceptors into a robotic prosthetic arm and utilize a deep learning algorithm to interpret the complex signal patterns. The system achieves accurate recognition of object interaction, validating the potential for naturalistic tactile feedback. This platform offers a scalable, biomimetic solution for developing next-generation sensory-augmented prostheses and may inform future designs in neuroprosthetics and human-machine interfaces.

ObjectivesThis scoping review maps evidence on sex and gender differences in technology-assisted stroke rehabilitation and identifies gaps to inform equitable approaches.Data sourcesSearches were conducted in two rounds (up to March 2025 and April 2025-March 2026) on MEDLINE, CINAHL, Scopus, Web of Science, Cochrane Library, and Google Scholar.Review methodsFollowing PRISMA-ScR guidelines, we included primary studies reporting sex and gender outcomes in adults with stroke receiving technological interventions, guided by the PCC framework.ResultsFrom 7071 records, 11 studies (1626 stroke survivors, 39.9% female) met inclusion criteria. Interventions included robotic exoskeletons, soft robotic gloves, virtual reality, exergaming, functional electrical stimulation, and biofeedback. Five studies found no significant sex differences. However, others reported female advantages in robotic gait training (p = 0.007), functional electrical stimulation (OR = 3.92), soft robotic glove hand function (3.43-fold greater improvement), and Virtual Reality-based balance recovery (p = 0.03). Younger women (<62 years) outperformed men in fine motor tasks, a pattern reversing with age.ConclusionGender and sex disparities in rehabilitation outcomes vary by technology type, underscoring the need for inclusive device designs and improved female representation in trials. Future research must integrate biological and sociocultural perspectives and standardize outcome measures to optimize tailored rehabilitation for all stroke survivors.

In this paper, a new solution of inverse kinematics was derived and programmed as an algorithm. The algorithm was coded and embedded in a MATLAB simulation program to manipulate a 5-DOF Humanoid Robotic Arm (HRA) and assess its reaching accuracy. The algorithm was designed as follows: joint frames were modelled based on the Denavit-Hartenberg (D-H) concept. Configuration for each joint frame was designed using the proposed D-H parameters. Forward kinematic (FK) equations were derived using the designed frames configuration. The inverse kinematic problem was solved (derived) using an analytical method to find five joint angles for the desired location and orientation. IK equations were derived from the FK transformation matrix for the given location and orientation to revers back the joint angles. The Graphical User Interface (GUI) was designed using MATLAB to simulate the proposed FK/IK algorithm. A group of desired locations was handled using the GUI to show the resultant pose of the HRA. The accuracy of the proposed IK was assessed by means of positional error. The lowest and highest average positional errors achieved were (0.026 and 0.79) cm, respectively.

We present an optimization-control framework for trajectory tracking of a 3-DoF manipulator, where an improved Stochastic Paint Optimizer (SPO-CL1) automatically tunes the gains of a sliding mode controller. Designed to address robotic challenges such as nonlinearities, couplings, and disturbances, the approach aims to achieve high tracking accuracy, rapid convergence, reduced chattering, and limited actuation effort, while remaining simple and robust to parameter variations. SPO-CL1 integrates three structure-aware enhancements into the original SPO architecture: chaotic initialization via the Chebyshev map to diversify the initial population, Opposition-Based Learning applied after the clustering phase to accelerate convergence, and periodic Lévy flight perturbations targeting the best solution to escape local optima. The SMC gains are automatically tuned by minimizing the Integrated Squared Error cost function J = ∫₀ᵀ eᵀ(t)e(t)dt. Two phases of validation were conducted. First, SPO-CL1 is benchmarked against eleven algorithms including recent hybrid variants (IGWO, EWOA, MHHO, HSMA) on the CEC-2022 suite, achieving the best Friedman rank of 1.83 and statistically significant superiority confirmed by Wilcoxon rank-sum tests (α = 0.05). Second, a path planning experiment on a Lemniscate of Bernoulli trajectory demonstrates that SPO-CL1 achieves the lowest ISE of 1.51 × 10⁻⁴ with near-zero inter-run variance, the fastest tracking error convergence, and the tightest end-effector trajectory among all twelve compared algorithms. These results confirm that SPO-CL1 is a competitive and reliable approach for automatic SMC gain tuning in complex robotic applications.

Fume hoods protect chemists and the environment from hazardous vapours and airborne substances produced during experiments. They are standard in chemistry laboratories worldwide. However, fume hoods were designed for manual chemistry, and there are still relatively few robotic systems designed to operate within these inherently confined spaces. It is challenging to design robotic systems that can perform the same variety of operations within fume hoods that can be performed by a dexterous human chemist. Here, we present an automated platform comprising a robotic arm that can perform liquid handling, solid handling, capping/decapping, heating and stirring, filtration, and sample imaging within a standard laboratory fume hood (50 cm × 120 cm × 170 cm). The broad applicability of this system was demonstrated in two materials research problems (a dye-based porosity screening workflow and the synthesis of a porous organic cage) and in a phthalimide synthesis. The success of the synthesis workflows was validated offline by NMR, X-ray diffraction, mass spectrometry and FTIR.

The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association map between design variables and performance metrics using a comprehensive dataset of existing dexterous hands, then apply this map to translate explicit high-frequency dynamic targets into an optimized hardware configuration. The analysis reveals that the dominant principles for high-speed performance-tendon-driven transmission, proximal actuation, and lightweight rigid structures-closely mirror the biomechanical architecture of the human hand. Guided by this convergence, we develop the Beyond Hand, a 20-degree-of-freedom (DoF) anthropomorphic hand that preserves human-scale dimensions. Standardized frequency-response tests across all 15 joints show magnitude attenuation below 3 dB at 14 Hz and cutoff frequencies clustered around 10 Hz. In rhythm-game and Tetris-style manipulation tasks, the hand maintains over 90% accuracy at actuation frequencies up to 12 Hz. These results demonstrate that a performance-driven pathway can systematically elevate the dynamic capabilities of humanoid dexterous hands, offering a scalable framework for biomimetic robotic design.

Restoring hand function remains a major challenge for individuals with motor impairments following stroke. Noninvasive brain-computer interfaces (BCIs) aim to address this problem by translating neural signals into robotic assistance; however, control of individual fingers has not been demonstrated in BCI-naïve populations. In this study, we investigated whether individuals with stroke and no prior BCI experience could achieve finger-level robotic control using motor imagery. Nine stroke-affected participants performed real-time BCI tasks to control a robotic hand through imagined finger movements decoded from electroencephalography. On average, participants achieved decoding accuracies of 84% for two-finger tasks and 61% for three-finger tasks, demonstrating reliable control at the level of individual fingers. These results indicate that discriminable neural signals for fine motor control persist after stroke and can be leveraged using data-driven deep learning decoders. Sensor-level and source-level electrophysiological analyses further reveal patterns of stroke-related neural reorganization. Overall, these findings support the potential of noninvasive, finger-level BCIs for post-stroke robotic assistance.

Design and prototyping of a smart powered lifting robotic arm (SPLRA)

Total hip arthroplasty (THA) is the primary treatment for end-stage hip diseases, and its clinical outcomes mainly depend on the accuracy of prosthesis implantation. Robot-assisted THA (RTHA), with its advantages in prosthesis implantation, overcomes the uncertainty of component positioning in conventional surgery and has become a research hotspot in joint surgery in recent years. This article systematically reviews international RTHA systems such as ROBODOC and MAKO, as well as domestic RTHA systems including the ARTHROBOT Joint Replacement Surgical Robot and the LONGWELL THA Surgical Robot, comparing their technical characteristics and clinical application status. It also explores the medical application of industrial robotic arms such as WAM, KUKA, and UR and their innovative applications in THA, aiming to provide reference for the future development and clinical application of RTHA systems.

ABSTRACT Shape memory alloys (SMAs) are attractive for soft robotic actuation because of their compactness and high power density, yet their widespread application is limited by slow thermal recovery. Here, we introduce ionic wind cooling as a compact thermal‐management strategy to overcome this bottleneck and improve the cyclic actuation performance of SMA‐driven soft robotic systems. Two ionic wind configurations were designed and comparatively evaluated for localized cooling of SMA springs. Both significantly accelerated SMA recovery with only minimal additional power input, and the needle–ring configuration was selected for subsequent integration because of its favorable stability and compact geometry. By coupling SMA springs with an origami‐based compression–twisting mechanism, an actuator‐level soft twisting module capable of reversible bidirectional rotation exceeding 80° was developed. The cooling strategy was further extended to multiple SMA‐driven modules and a reconfigurable soft robotic arm capable of coordinated twisting, bending, and multimodal grasping. The results show that ionic wind cooling not only enhances the recovery of individual SMA actuators but also enables faster motion switching and improved cyclic response at the system level. This work demonstrates ionic wind cooling as a compact and effective strategy for enhancing the dynamic performance of SMA‐driven soft robotic systems.

Optoelectronic synaptic devices enable in-sensor processing of enhanced edge detection and contrast resolution in complex visual scenes due to their excellent capability to emulate the functions of visual neurons, such as light perception and image processing, while lateral inhibition synaptic plasticity refines spatial selectivity and extends the dynamic range by suppressing redundant signals and amplifying subtle variations in input intensity. The incorporation of lateral inhibition into a single optoelectronic synaptic device will offer a cost-effective and energy-efficient route for directing a robotic arm to perform responding motions and developing highly efficient machine vision systems. Herein, we demonstrate an optoelectronic artificial synapse established on a novel heterostructure consisting of metal oxide In2O3, polycrystalline Cs2AgBiBr6perovskite, and indium-gallium-zinc oxide thin film, which enhances the optoelectronic response and corresponding synaptic plasticity of the devices, enabling the emulation of neural behaviour and advanced information processing. The structure simulates excitatory synaptic activity through light stimulation and mimics lateral inhibition through electrical stimulation, effectively replicating the neural mechanisms of synaptic plasticity in processes such as Mach bands, contrast enhancement, and Hermann's grid. Leveraging these properties, we develop a lateral inhibition network for image recognition, achieving 97% accuracy-surpassing conventional networks at 93%. Additionally, through seamless integration with robotic arms, it can execute colour chip recognition on a machine cart, providing a promising strategy for the design of intelligent autonomous devices and bioinspired robots.

A novel methodological framework for the assessment of the neural control of the shoulder using high-density surface electromyography.

Time-varying quadratic programming (TVQP) requires efficient, accurate, and robust online solvers. Existing discrete-time (DT) recurrent neural networks (RNNs), however, often face a tradeoff between solution precision and noise immunity. To address this issue, a sliding integral neural network (SINN) is proposed. In particular, the interior-point (IP) method is extended to a dynamic IP (DIP) formulation with a time-varying barrier coefficient, and TVQP is reformulated as a DT error-feedback system for closed-loop design. A sliding integral control law with forward harmonic vectors is then developed to compensate for iterative differential residuals. Theoretical analyses show that the SINN achieves $\boldsymbol {O}(\tau ^{4})$ steady-state accuracy, where $\tau $ denotes the sampling interval, and suppresses structured disturbances with sublinear/linear growth. Numerical and robotic arm motion-planning simulations demonstrate its high precision and robustness.