Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 18 appraisals
Bioinspiration & biomimetics
Length control of pneumatic artificial muscles inspired by intrafusal-extrafusal muscle interactions
Pneumatic artificial muscles (PAMs) are widely used in pneumatic musculoskeletal robots due to their lightweight nature and high power output. On the other hand, their control typically relies on electro-pneumatic valves, electronic sensors, and peripheral circuits such as A/D converters. This increases the number of distributed electronic components and electrical interconnections, potentially leading to greater system mass as well as higher maintenance requirements, particularly in systems containing many PAMs. To overcome these challenges, inspired by the intrafusal-extrafusal muscle interactions in biological systems, this paper proposes a length control method for PAMs in which the pneumatic feedback loop is implemented mechanically, while the target length is commanded by a minimal electronic actuator. We first examined flow path conditions to suppress oscillations caused by pneumatic delays. Second, Experiments demonstrated a tracking error of2.01% for input frequencies0.20 Hz.
29 July 2026
Read appraisal →Journal of neural engineering
Cortical-SSM: a deep state space model for motor imagery decoding from EEG signals
Objective.Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g. eye blinking, swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG signals have been widely adopted, they often struggle to capture fine-grained dependencies within them.Approach.To overcome these limitations, we propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG signals across temporal, spatial, and frequency domains. We validated our method across two large-scale public MI EEG datasets containing more than 50 subjects.Main results.Our method outperformed baseline methods on the two benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of EEG signals.Significance.These results indicate that Cortical-SSM provides a robust and interpretable alternative to attention-based architectures for MI EEG decoding. By enabling physiologically grounded feature learning, our method advances the reliability of subject-independent EEG classification and supports the development of practical and clinically deployable brain-computer interface systems.
26 July 2026
Read appraisal →Nature communications
Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control
Brain-computer interfaces (BCIs) offer the potential to restore function and augment human capabilities. However, non-invasive electroencephalography (EEG)-based BCIs still face challenges in learning efficiency and control precision, particularly for naïve users performing complex tasks. Here, we present a sensory-guided joint learning framework that integrates human motor learning with adaptive machine learning to improve BCI training and performance. In 31 BCI-naïve participants, the framework enabled rapid skill acquisition, achieving average online discrete accuracies of 86.0% for one-dimensional (1D) and 77.5% for two-dimensional (2D) motor imagery tasks, along with continuous control accuracies of 77.5% (1D) and 66.9% (2D). Mechanistically, tactile guidance reduced user exploration and accelerated neural adaptation, while sample reweighting aligned decoder updates with human learning trajectories. By coupling reinforcement-driven neural plasticity with adaptive algorithmic optimization, this framework advances BCI training from passive calibration to active human-machine joint learning, enabling practical and scalable neural interfaces for communication and rehabilitation.
17 July 2026
Read appraisal →Science advances
Transforming static interfaces into tactile channels with steerable transdermal foci
Grasping objects such as handles, mice, and phones plays a central role in human-machine interaction, yet the contact surfaces of the hand, despite their exquisite tactile sensitivity, are rarely exploited as channels for information transfer. Here, we introduce transdermal all-directional targeting (TADT), a static-contact interface design strategy that converts passive grip surfaces into active tactile displays, allowing users to grasp stably while receiving spatially and temporally evolving information on the fingertips. TADT enhances sub-actuator-pitch energy delivery by integrating a microtextured skin-coupling interface with phase-controlled vibrotactile actuation, thereby generating steerable three-dimensional tactile foci beneath the skin, enabling >200-hertz vibration and sub-actuator-pitch spatial super-resolution. Psychophysical experiments show that TADT lowers detection thresholds by 30% and reduces power consumption by 80% compared with designs without TADT. Using static grip alone, users reliably identified six directional cues and completed nonvisual indoor navigation and virtual reality interaction tasks, demonstrating a compact, energy-efficient modality for expressive tactile communication.
12 July 2026
Read appraisal →ACS applied materials & interfaces
Biodegradable Electrohydraulic Actuators for Wearable Haptic Interfaces
Biodegradable soft actuators have gained significant attention due to their inherent conformability, adaptability, and ability to safely interact with humans, while substantially reducing long-term environmental waste. Although various biodegradable materials have been explored for fabricating soft devices and their electrical interfaces, many still suffer from limited durability and operational stability under ambient conditions. In particular, biodegradable electrohydraulic actuators often exhibit a performance degradation over time. In this work, we systematically evaluate the influence of various biodegradable materials, including compliant electrodes, dielectric films, and dielectric fluids, on actuator performance. The optimal material combination was identified and used to fabricate a biodegradable actuator that demonstrated stable operation over 10,000 cycles with no noticeable variation in actuation strain. Under industrial composting conditions, the device underwent degradation within 50 days, supporting its environmental sustainability. Furthermore, a wrist-worn biodegradable haptic device is demonstrated that is capable of generating diverse and salient tactile sensations, highlighting its potential applications in assistive technology and virtual/augmented reality.
25 June 2026
Read appraisal →Bioinspiration & biomimetics
A bioinspired hybrid robotic joint for safer physical human-robot interaction
In many robotic applications, such as humanoids, wearable robots or supernumerary limbs, there is a growing shift from rigid, traditional mechanisms toward softer, more compliant systems. This trend is driven by the need for safer physical human-robot interaction and the ability to operate in unstructured environments. In this work, we present a hybrid approach to controlling a single-degree-of-freedom robotic joint that combines a rigid frame with soft pneumatic actuators to enable both precise and versatile interaction. The joint is bioinspired, as it is powered by a couple of actuators mounted in an antagonist configuration, such as the human elbow. A key innovation is the design of pneumatic artificial muscles using thermoplastic polyurethane, which achieve high isometric force (400 N at 240 kPa), significant stretchability (80 mm), and low density(0.3gcm-3), making them competitive with state-of-the-art alternatives. Two model-free controllers were developed to independently regulate joint position and stiffness. Angle control achieved high precision (<2∘RMSE) with minimal overshoot (<1%) and fast response (rise time<1.3s). Stiffness control modulated joint compliance across a range of 0.054-0.076 Nm deg-1, with the expected trade-off of reduced workspace. A final proof-of-concept demonstrated the concurrent use of both controllers to modulate the joint's dynamic behavior in response to external disturbances. While future work will address multi-DOF coordination and wearable integration, this study represents a foundational step toward safe, adaptable robotic actuation through the combination of rigid structures and soft actuation.
21 June 2026
Read appraisal →ACS applied materials & interfaces
Breathable and Washable E-Textile with Readily Integrated Piezoelectric Perfluoroalkoxy Fiber for Wide Temperature Range Human Machine Interfaces
Flexible wearable sensors are widely explored for human-machine interaction and health monitoring. Electronic textiles (E-textiles) offer a promising pathway toward comfortable wearable devices by integrating sensing fibers directly into fabric structures. Among them, compared with piezoresistive and capacitive fiber sensors, piezoelectric fiber sensors require no external power supply. Compared with triboelectric fiber sensors, which are highly sensitive to humidity and temperature, piezoelectric fiber sensors provide more stable and reliable electrical signals. However, conventional piezoelectric fiber sensors still face challenges in achieving long-term wearing comfort and reliability under harsh environments. Herein, we develop a breathable, washable, and wide-temperature-tolerant based on piezoelectric perfluoroalkoxy (PFA) fibers, which can be readily integrated into E-textiles. The textile is produced via a weft knitting process that integrates coaxially structured PFA fibers with wool yarns, combining stable piezoelectric performance with wearing comfort. The fibers exhibited a charge sensitivity of (2.6 ± 0.2) pC/N at 0.2 MPa. The resulting E-textile exhibits a charge sensitivity of up to (316.6 ± 45.9) pC/N and a voltage sensitivity of (744.9 ± 73.9) mV/N. The textile maintains stable operation over a wide temperature range from -78 °C to 150 °C. Its weft-knitted structure provides breathability, while the textile also offers washability, UV resistance, and mechanical durability exceeding 10,000 stretching or bending cycles. Furthermore, the textile can effectively monitor various human motions. A smart glove constructed from five-channel PFA fibers achieved recognition accuracies above 95% for five hand gestures, demonstrating the potential offer self-powered E-textile as a wearable sensor for human-machine interaction.
30 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Soft pneumatic actuators for wearable systems: a structural classification and integration-oriented evaluation
Soft pneumatic actuators promise gentle, body-safe assistance, yet many fail at the moment of integration: inflation alters geometry, increases profile, and stresses seams and ports. This topical review reframes actuator selection through a structure-first lens that links how an actuator is built to how it deforms and how it embeds in garments. Actuators are classified by structural architecture, deformation behavior, and fabrication method, and evaluated against four integration criteria central to wearable systems: geometric compatibility, fabrication scalability, system integration readiness, and actuation simplicity. A staged selection pathway is proposed and presented as a literature-informed mapping table that links reported wearable application contexts to dominant integration priorities and a practical structural-class starting point. Planar sheet designs can preserve a thin, predictable pressurized envelope at modest pressures when seam paths, constraint layers, and attachment features are co-designed. Pouch and bladder actuators are thin at rest, simple to fabricate, and readily integrated with textiles, but commonly bulge under load without external constraint; envelope control and port or manifold routing frequently limit garment integration. Fiber-constrained actuators deliver high specific force but often require rigid end terminations and elevated pressures. Segmented elastomers provide rich kinematics through chamber layout while tending toward bulging and routing burden in multi-segment formats. Mechanical metamaterials realize geometry-programmed motion when hinge fidelity and pattern alignment are maintained; durability of compliant joints and consistent crease formation set current limits. The resulting synthesis identifies practical priorities for wearable use: preserve thin profiles under load, embed stitchable or bondable attachment features, document reproducible process windows, minimize dead volume, routing, and valve count with compact pneumatic architectures, and target low-pressure operation compatible with lightweight hardware. Framing integration in structural terms standardizes comparison across actuator classes, clarifies the narrow feasible design space for compact, body-conforming devices, and supports deliberate actuator choice for biomedical wearables.
29 May 2026
Read appraisal →ACS nano
All-Printed MXene-Based Springs for Concurrent Bidirectional Hand Motion Capture
Accurately capturing hand movements remains a challenge because the skin undergoes stretching, compression and bending at the same time, yet most flexible sensors respond only to tension. Achieving high sensitivity and a wide range often means making the sensor thick, which causes the sensing layer to shift under complex deformations and distorts the signal. Here, we address this by placing the strain-accommodating space within a plane parallel to the strain direction. Using a MXene/polyurethane ink with glycerol as a molecular bridge, we create a dense hydrogen-bonding network that enables high-resolution printing of a planar spring architecture. The resulting all-printed sensor achieves a gauge factor of 83.7 across a linear bidirectional range of ±60% strain. With just two sensors, we track and decode complex wrist motions with ±10° accuracy. When attached to finger joints, they enable real-time, dexterous control of a virtual hand. This work provides an alternative approach for skin strain capture through synergistic innovation in materials and architecture.
27 May 2026
Read appraisal →Science advances
An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.
Sign language translation systems have long aimed to bridge the communication between signers and nonsigners. However, preliminary systems rely on glove-type wearables or wired sensor arrays, which constrain hand movement, reduce comfort, and require nonpersonalized sensor positions that limit adaptability across users. Here, we introduce a wirelessly connected, ring-type sign language translator (WRSLT) designed to overcome these limitations by enabling full finger mobility through independent sensor rings and multilink communication. The system supports static and dynamic gesture detection using selected fingers via quantitative relevance analysis and achieves robust user-independent performance without per-user calibration. WRSLT demonstrated high recognition accuracy on large-scale datasets comprising 100 American Sign Language and 100 International Sign Language words, achieving 88.3 and 88.5% accuracy, respectively, under unseen-user conditions (i.e., test users not included in model training). Furthermore, a custom sequential word detection framework enables sentence-level translation from continuous signing input without requiring separate training on entire sentence structures.
5 May 2026
Read appraisal →PloS one
RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features
Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.
26 Apr 2026
Read appraisal →Science advances
Wearable thumb sleeves enabled by self-supervised learning with few stretchable sensors and few-shot data for switchable finger tasks
Human fingers exhibiting remarkable dexterity are ideal for natural human-machine interaction. Traditional methods require at least one device per finger and extensive labeled data, often limiting models to a single user and task. Here, we propose a wearable thumb sleeve integrated with self-supervised learning, which exhibits user independence and data efficiency, enabling recognition of various finger-related tasks. The thumb sleeve is equipped with only two stretchable sensors at the thumb joints and learns latent features from unlabeled random thumb movement data. By using fine-tuning with five-shot labeled data, it can rapidly adapt to new users and tasks, including eight directional commands and 10 knuckle key inputs. It allows free switching between tasks without the need to reconstruct or retrain the model. The proposed approach demonstrates strong potential for real-world applications, serving as a substitute for a mouse and keyboard to enable tasks such as online shopping.
25 Apr 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Navigation paradigms for non-invasive BCI-controlled wheelchairs: a systematic review
Brain-controlled powered wheelchairs represent a promising advancement for individuals with neurological conditions that significantly impair motor function. Despite substantial progress, brain-controlled wheelchairs have not been adapted for real-world settings. This article systematically reviews recent trends in brain-computer interface (BCI) technology for wheelchair navigation and control, highlighting the contributions and limitations of various navigation paradigms. The review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, sourcing studies from four databases (PubMed, Scopus, IEEE Xplore, Google Scholar) published between 2000 and April 2025. It focused on non-invasive BCI paradigms and real-world navigation experiments. The results were narratively synthesized and classified into two primary categories: BCI-based navigation paradigms and wheelchair-based navigation paradigms, along with intersecting concepts such as single-variant BCI, hybrid BCI, control switches, and proportional control. Of the 149 full-text articles reviewed, 47 were included and categorized by navigation paradigm, comprising 20 BCI-based and 27 wheelchair-based studies, with 6 involving participants with motor disabilities. Quality assessment scores ranged from 40% to 95%, with approximately 40% of the studies demonstrating a low risk of bias. The findings indicate that low-level navigation control was predominant in BCI wheelchair studies, with 31 studies employing minimal or no obstacle avoidance. Most studies (57%) integrated sensors for obstacle avoidance, localization, mapping, and autonomous navigation. Twenty-two studies utilized control switches, and five incorporated proportional control for wheelchair navigation. Additionally, motor imagery and steady-state visually evoked potential paradigms have emerged as the most common approaches for generating control commands, highlighting their potential for effective navigation. Given the potential societal impact on a large number of individuals, future research should prioritize enhancing the reliability and adaptability of BCI wheelchair systems in real-world environments.
16 Apr 2026
Read appraisal →PloS one
MSCNet: Efficient and accurate semantic segmentation of LiDAR data using Multi-scale Convolution
In autonomous driving and intelligent robotics, the semantic information of LiDAR (Light Detection and Ranging) sensor data is crucial for understanding the surrounding environment. However, directly operating on point clouds is computationally expensive. To address this, some researchers have projected three-dimensional LiDAR data onto a two-dimensional spherical range view and used two-dimensional convolutional neural networks to segment the projected images. While the results are promising, many of these models are structurally complex, with high spatiotemporal complexity, which makes them unsuitable for real-time applications. To solve these issues, this paper proposes a multi-scale LiDAR data semantic segmentation method, MSCNet, with fewer parameters and higher segmentation accuracy. In the encoding phase, a single-channel multi-scale feature fusion block is introduced to alleviate the distribution differences between input channels. To obtain more stable local features, multi-scale dilated convolution residual blocks are designed to encode information from different receptive fields. To quickly capture global features, a pyramid pooling module is introduced. Experimental results on the SemanticKITTI, SemanticPOSS, and Pandaset datasets show that MSCNet achieves a good balance between parameter, accuracy, and running time. Particularly on the SemanticPOSS and Pandaset datasets, MSCNet achieves the best performance. Under the same parameter conditions, this method outperforms existing point cloud-based and projection-based methods.
10 Apr 2026
Read appraisal →Science advances
Filament sensing tension: A bionic artificial tendon for self-force-regulated artificial muscle-driven wearable robotics
Artificial muscles have been in development for decades and play a crucial role in the field of wearable robotics. However, a universal device for both sensing and regulating forces in artificial muscles is still absent, rendering scenario-required force control unfeasible. Inspired by Golgi tendon organs, this work proposes bionic ExoTendon for force sensing and regulating in artificial muscles as a general solution. Leveraging the principles of triboelectrification and electrostatic induction, the ExoTendon offers high linearity, minimal hysteresis, and adjustable sensitivity and range, making it suitable as the tendon of artificial muscles. Specifically, the ExoTendon effectively enable adjustment to the pretension of the artificial muscles, yielding various assistive forces under the same input. With the optimized pretension level and closed-loop force control, the artificial muscle-based exosuit achieves self-force regulation, substantially improving walking balance and speed in stroke subjects under low input, demonstrating its promise in wearable robotics and rehabilitation medicine.
9 Apr 2026
Read appraisal →Journal of neural engineering
Large-scale training data enhances silent speech decoding with around-ear EEG.
Objective. Silent speech decoding (SSD) offers a potential communication alternative for individuals with impaired vocalization. However, conventional multi-electrode electroencephalography (EEG) or facial electromyography (EMG) systems require cumbersome preparation and are unsuitable for daily use. This study evaluates the practicality of SSD using a wearable around-ear EEG device, focusing on data scaling, cross-subject transfer, vocabulary extensibility, and online decoding performance.Approach. We collected 72 h of around-ear EEG from 24 healthy participants and one individual with incomplete locked-in syndrome (LIS) during silent, vocalized, and attempted speech, and integrated these around-ear EEG recordings with prior EMG + high-density EEG datasets, yielding 282.4 total h of training data. Using a 64-word classification task as the evaluation metric, we assessed: (1) whether larger datasets improve around-ear EEG-based SSD, (2) whether healthy-participant data supplement limited LIS-participant data despite articulatory differences, (3) transferability to unseen vocabulary, and (4) online user-interface performance.Main results. Large-scale EEG/EMG data improved SSD accuracy in both healthy participants and the LIS participant. Training on the heterogeneous dataset achieved 56.6% accuracy for healthy users and 47.3% for the LIS participant. Fine-tuning this decoder for new vocabulary increased the accuracy by 22 percentage points relative to training from scratch. Regression analysis showed that, for decoding in the LIS participant, data from the LIS participant contributed approximately four times the weight of healthy-participant data, quantifying data strategies for SSD. Online experiments achieved top-1/top-5 accuracies of 47.2%/76.0% for healthy users and 26.5%/49.1% for the LIS participant.Significance. The results indicate that lightweight, commercially feasible around-ear EEG can enable practical SSD when combined with large-scale healthy-participant data, supporting online operation. Moreover, models trained on a 64-word vocabulary facilitate decoding of a new vocabulary, providing a path toward SSD systems requiring minimal LIS-participant data. This study advances non-invasive SSD systems suitable for everyday communication.
8 Apr 2026
Read appraisal →Journal of neural engineering
EDAPT: towards calibration-free BCIs with continual online adaptation
Objective.Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. Our goal is to develop a framework that eliminates the need for separate calibration phases by enabling continual, real-time model adaptation to new users and changing signal characteristics.Approach.We propose EDAPT, a task- and model-agnostic framework for continual online learning. EDAPT first establishes a robust baseline decoder through population-level pretraining (PRE) on data from multiple users. It then personalizes this model during deployment using supervised continual finetuning on a trial-by-trial basis. Due to its modular design, EDAPT can be composed with unsupervised domain adaptation (UDA) techniques to further address distribution shifts.Main results.We validate EDAPT across nine datasets, three BCI paradigms, and four deep learning architectures. EDAPT consistently improves decoding accuracy over static models for nearly all subjects and datasets, raising mean balanced accuracy from 0.80 to 0.87 on representative datasets (table3). Ablation studies confirm that the combination of PRE and online finetuning is the primary driver of this performance gain, with further improvements on some datasets when using UDA techniques. We demonstrate real-time feasibility of the framework, with adaptation latencies under 200 ms on consumer-grade hardware. Our scaling analysis further reveals that decoding accuracy is primarily determined by the total pretraining data budget, rather than its specific allocation between subjects and trials.Significance.These findings demonstrate that continual online learning is a practical and effective strategy for creating high-performance, user-adaptive BCIs. By systematically addressing the bottleneck of model recalibration, EDAPT reduces a major barrier to the widespread adoption of BCI technology and helps advance neurotechnology toward robust, user-friendly, real-world applications.
8 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wireless, Battery-Free Artificial Throat Patch with Deep Learning for Emotional Speech Recognition
Technologies for sensing and translating throat signals into speech have advanced the communication for people with vocal impairments. However, the practical deployment of these technologies is currently hindered by tethered system, bulky components and the lack of emotional recognition capability. Here, we demonstrate a wireless, battery-free artificial throat patch system (ATPS) coupling with deep learning method to enable simultaneous recognition of speech and emotion. The sensing module of the ATPS integrates a carbon nanotube-based thin-film strain sensor and a miniaturized flexible printed circuit board (FPCB). Real-time sensing and transmission of throat signals is facilitated by a smartphone-linked system incorporating an FPCB with an embedded near-field communication antenna and low-power electronic components. Furthermore, the information-rich sensor readouts are further extracted and successfully demonstrated to be sufficient to emotional recognition of speech by implementing a hybrid deep learning architecture. This proposed system expands the arsenal of tools available for mute patients in speech recognition and communication and further realizes personalized clinical implementations.
4 Apr 2026
Read appraisal →