搜索结果：npj Robotics

Accurate indoor positioning is vital for applications such as augmented reality and autonomous robotics. Channel state information (CSI)-based methods, particularly when combined with beamforming, massive multiple input multiple output (mMIMO) techniques, and artificial intelligence (AI) algorithms, offer enhanced indoor user equipment (UE) positioning accuracy and robustness in complex indoor environments. In this paper, we present an AI-driven CSI-based indoor positioning method for mMIMO systems, where channel impulse, channel frequency, and angular response domain features are extracted from the CSI data and combined to form both uni-domain and multi-domain feature sets. We introduce a deep attention network (DAN), an AI algorithm that leverages attention mechanisms to effectively integrate and process multi-domain CSI data, thereby enhancing UE positioning performance. We evaluate DAN using a publicly available mMIMO dataset and compare its performance against the baseline and multi-domain convolutional neural network (CNN) models. Our results show that multi-domain DAN outperforms CNN approaches in positioning accuracy, though at the cost of increased inference complexity-highlighting a trade-off between performance and computational overhead. These findings demonstrate the potential of attention mechanisms and multi-domain CSI features for accurate indoor UE positioning systems.

The Nobel Turing Challenge (NTC) proposes AI systems capable of autonomous, Nobel-level scientific discovery. The IJNTC2025 workshop convened researchers from India and Japan to advance this goal in health and biomedicine. This perspective synthesizes four themes: knowledge extraction, laboratory automation, hypothesis generation, and equitable healthcare applications. It identifies how Japan's robotics and precision AI expertise and India's large-scale data infrastructure and frugal innovation offer complementary strengths for collaborative progress toward the NTC.

Synthetic data generation across domains can bridge gaps between visual training, skill development, and personalized surgical planning, ultimately transforming how surgeons and artificial intelligence (AI) systems prepare for the complexities of the operating room. In this Perspective, we explore applications of synthetic data to advance surgical education and AI across three key areas: visual data synthesis for training surgeons and AI systems, surgical simulation for skill development and robotics, and digital twins for patient-specific surgical planning and guidance. These domains have largely remained siloed, but their integration has the potential to transform surgical training and AI development across the entire surgical workflow. To fully realize this potential, synthetic data must extend beyond routine surgical events to model atypical anatomy and intraoperative complications-the high-stakes clinical scenarios where enhanced training and AI support are most critical.

Human cognitive impairment associated with sleep loss, circadian misalignment and work overload is a major concern in any high stress occupation but has potentially catastrophic consequences during spaceflight human robotic interactions. Two safe, wake-promoting countermeasures, caffeine and blue-enriched white light have been studied on Earth and are available on the International Space Station. We therefore conducted a randomized, placebo-controlled, cross-over trial examining the impact of regularly timed low-dose caffeine (0.3 mg per kg per h) and moderate illuminance blue-enriched white light (~90 lux, ~88 melEDI lux, 6300 K) as countermeasures, separately and combined, in a multi-night simulation of sleep-wake shifts experienced during spaceflight among 16 participants (7 F, ages 26-55). We find that chronic administration of low-dose caffeine improves subjective and objective correlates of alertness and performance during an overnight work schedule involving chronic sleep loss and circadian misalignment, although we also find that caffeine disrupts subsequent sleep. We further find that 90 lux of blue-enriched light moderately reduces electroencephalogram (EEG) power in the theta and delta regions, which are associated with sleepiness. These findings support the use of low-dose caffeine and potentially blue-enriched white light to enhance alertness and performance among astronauts and shiftworking populations.

The electrical stimulation of the nervous system has shown great clinical potential in injury and pathology, yet experimentally driven practice makes it challenging to identify effective design choices and personalized stimulation protocols. This review outlines emerging model-based optimization frameworks that address these challenges by leveraging biophysical digital twins of neural interfaces. Enabling acceleration strategies and complementary data-driven approaches are also highlighted, along with key factors that currently limit clinical translation.

Gripping to smooth and wavy substrates, such as naturally occurring ice, presents a challenge for climbing robots in the field. Existing ice anchoring solutions require either substantial initial surface compression force (drilling; at least 50 Newtons) or require large energy expenditure (thermal picks; almost 1000 Joules). We present an anchoring mechanism capable of attaching to ice with lower initial surface compression force and lower energy consumption compared to drill-based or melt-based methods. The system leverages surface fracture caused by dynamic impacts with axes - inspired by mountaineers - to create indentations for grasping. A model describes the indentation depth, recoil energy, and surface compression force required for anchoring success, each as a function of impact energy. An integrated dual-ax gripper system successfully generates usable indents with as low as 8.3 Newtons of initial surface compression force and 8 Joules of combined mechanical potential energy on -14∘ C freshwater ice - a result consistent with first-principle model predictions. The gripper then successfully holds its own weight on steep glacier slopes in the field. These results indicate fracture-based grasping approaches are promising for climbing systems on ice. This concept can also apply to other surfaces such as wood, rock, and packed soil.

Neuroinflammation contributes to the progression of many neurological diseases. Here, we explore whether ultrasound can reduce microglia-mediated inflammation in vitro and in vivo. We tested a broad range of ultrasound parameters in a BV2 microglial cell line, treated with lipopolysaccharide (LPS) to induce an inflammatory response. We found that specific combinations of centre frequency, acoustic pressure and treatment duration can significantly lower the levels of pro-inflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6. These effects lasted up to 72 h and were associated with the downregulation of the nuclear factor κB (NF-κB), suggesting a mechanistic link between ultrasound and inflammation. Further investigation in vivo, in LPS-treated mice, revealed a reduction in TNF-α expression in the hippocampus following ultrasound. Overall, our findings showcase the potential of ultrasound as a non-invasive therapeutic strategy to reduce neuroinflammation and restore brain homeostasis.

Coordinated trajectory planning is essential for multi-robot applications, ranging from factory automation to entertainment. The main challenge is providing long-term coordination guarantees, such as freedom from collisions, deadlocks, and livelocks, as well as kinodynamic agility, especially in densely populated environments. Although continuous optimization provides agility, it is computationally expensive. In contrast, discrete search is scalable but lacks physical realism for robot execution. This study introduces concrete planning, a hybrid approach that captures real-world continuous dynamics while maintaining scalable guaranteed planning via discrete search. We integrate recent advances in robot dynamics learning, optimal control, and anytime complete planning into a modular framework. The framework is deployed with 40 robots, including 20 aerial, 8 ground, and 12 obstacle robots, operating in a compact laboratory space. Despite the dense and time-varying setup, the robots achieve consecutive navigation missions on-demand, while executing aggressive maneuvers that substantially reduce task completion time.

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and validated Multi-Phase Attention Network (MPANet), a multimodal deep learning model integrating multiphase contrast-enhanced CT and clinical information, which can utilize both complete-phase and missing-phase CT data for multiclass classification of four common and easily confusable renal tumors-clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), oncocytic neoplasms (including chromophobe renal cell carcinoma (chRCC) and renal oncocytoma (RO)), and fat-poor angiomyolipoma (fpAML). A total of 1688 multi-center cases were enrolled. Across all test sets, MPANet consistently outperformed single-phase models. In the internal test set, MPANet achieved a macro-average AUC of 0.850, a micro-average AUC of 0.865, and an accuracy of 73.3%. These results compared favorably to assessments by four radiologists based on CT (accuracies 43.6-62.4%) and two radiologists using MRI with clear cell likelihood score (ccLS) system (accuracies 52.5% and 49.5%). The net improvement rate of MPANet over radiologist assessment ranged from 10.9% to 29.7%. In the two external test sets, macro-average AUCs were 0.811 and 0.813, and micro-average AUCs were 0.867 and 0.909, respectively. MPANet shows potential as a clinical decision-support tool for personalized renal tumor diagnosis.

Distinguishing epileptic seizures from parasomnias is challenging due to overlapping motor features. This study evaluated a SlowFast deep learning model using video recordings of 167 individuals to classify Sleep-Related Hypermotor Epilepsy, Disorders of Arousal, and REM Sleep Behavior Disorder. The model achieved a mean accuracy of 83.3% across three data splits. This work represents an initial step toward developing automated tools to support clinicians in assessing sleep-related motor events.

Artificial intelligence (AI) is increasingly used in gastrointestinal endoscopy for polyp detection and classification. However, most AI models are trained on images from multiple video processors, whereas clinical environments typically rely on a single processor. We developed EndoStyle, a StarGANv2-based style transfer system trained to mimic the visual characteristics of five different endoscopic processors. On a validation dataset, Fréchet Inception Distance and Learned Perceptual Image Patch Similarity indicated high visual fidelity and perceptual similarity across processors. Semantic similarity analysis using three foundation models confirmed that converted images were equally consistent with both content and style inputs. In a multicenter study, endoscopists considered real and converted images realistic at comparable rates. When used to augment polyp detection model training, synthetic images significantly improved precision and specificity, reducing false positives by over 40% on two distinct evaluation datasets. EndoStyle thus offers a practical solution for processor-specific AI generalization.

Vision is an essential part of attitude control for many flying animals, some of which have no dedicated sense of gravity. Flying robots, on the other hand, typically depend heavily on accelerometers and gyroscopes for attitude stabilization. In this work, we present the first vision-only approach to flight control for use in generic environments. We show that a quadrotor drone equipped with a downward-facing event camera can estimate its attitude and rotation rate from just the event stream, enabling flight control without inertial sensors. Our approach uses a small recurrent convolutional neural network trained through supervised learning. Real-world flight tests demonstrate that our combination of event camera and low-latency neural network is capable of replacing the inertial measurement unit in a traditional flight control loop. Furthermore, we investigate the network's generalization across different environments, and the impact of memory and different fields of view. While networks with memory and access to horizon-like visual cues achieve best performance, variants with a narrower field of view achieve better relative generalization. Our work showcases vision-only flight control as a promising candidate for enabling autonomous, insect-scale flying robots.

Although muscle mass loss is an emerging public health concern, its prevalence, associated factors, and clinical significance in Parkinson's disease (PD) remain unclear. This matched case-control study aimed to investigate the prevalence of low muscle mass (LMM) and to examine its association with orthostatic hypotension (OH) and orthostatic symptoms in 409 PD patients with Hoehn and Yahr stage ≤3, compared with 2045 age-, sex-, and height-matched controls from a nationwide database. OH was defined according to the international consensus. LMM was more prevalent in PD patients than in controls, particularly among men and those aged ≥70 years. Among PD patients, the prevalence of OH did not differ between those with and without LMM. Although LMM was linked to greater orthostatic blood pressure reductions at 30 s after standing, there were no differences in the frequency or severity of orthostatic symptoms according to LMM status. These findings suggest that although mild to moderate PD is associated with an increased risk of LMM, its impact on OH and related symptoms appears to be modest. Further longitudinal studies are needed to clarify the clinical implications of LMM in PD.

We aimed to establish a robust vision-language model ("Glio-LLaMA-Vision") for molecular status prediction and radiology report generation (RRG) in adult-type diffuse gliomas. Multiparametric MRI data and paired radiology reports from 1001 patients with adult-type diffuse gliomas were included in the institutional training set. A vision-language model, Glio-LLaMA-Vision, was developed from LLaMA 3.1 pre-trained on 2.79 million biomedical image-text pairs from PubMed Central and further fine-tuned from the institutional training set. The performance was validated in 100 patients and 75 patients with paired MRI-radiology reports from an institutional validation set and another tertiary institution (AMC), and in 170 and 477 patients with MRI from TCGA and UCSF datasets, respectively. In terms of IDH mutation status prediction, Glio-LLaMA-Vision showed AUCs ranging from 0.85-0.95 in the internal validation and external datasets. In terms of RRG, the BLEU-1 and ROUGE-L scores were 0.50 and 0.49 in the internal validation, respectively, and 0.32 and 0.36 on the AMC dataset, respectively. Overall, 37.8% of generated reports were considered superior or equal to the original reports, while 91.0% of generated reports were considered clinically acceptable by neuroradiologists. In conclusion, Glio-LLaMA-Vision demonstrates promising performance in molecular status prediction and RRG in adult-type diffuse gliomas, showing potential for clinical assistance.

The integration of robotics in surgery has increased over the past decade, and advances in the autonomous capabilities of surgical robots have paralleled that of assistive and industrial robots. However, classification and regulatory frameworks have not kept pace with the increasing autonomy of surgical robots. There is a need to modernize our classification to understand technological trends and prepare to regulate and streamline surgical practice around these robotic systems. We present a systematic review of all surgical robots cleared by the United States Food and Drug Administration (FDA) from 2015 to 2023, utilizing a classification system that we call Levels of Autonomy in Surgical Robotics (LASR) to categorize each robot's decision-making and action-taking abilities from Level 1 (Robot Assistance) to Level 5 (Full Autonomy). We searched the 510(k), De Novo, and AccessGUDID databases in December 2023 and included all medical devices fitting our definition of a surgical robot. 37,981 records were screened to identify 49 surgical robots. Most surgical robots were at Level 1 (86%) and some reached Level 3 (Conditional Autonomy) (6%). 2 surgical robots were recognized by the FDA to have machine learning-enabled capabilities, while more were reported to have these capabilities in their marketing materials. Most surgical robots were introduced via the 510(k) pathway, but a growing number via the De Novo pathway. This review highlights trends toward greater autonomy in surgical robotics. Implementing regulatory frameworks that acknowledge varying levels of autonomy in surgical robots may help ensure their safe and effective integration into surgical practice.

Food security faces growing challenges due to population growth, resource limitations, economic pressures, and industrialization-induced lifestyle changes. Traditional food systems struggle to adapt, necessitating innovative solutions and sustainable practices to meet future food demands. This review article explores emerging food system models and alternative food sources, including edible insects, seaweeds, plant-based and lab-cultured meats, underutilized crops, hydroponics, and next-generation fish farming. It highlights the role of food processing technologies such as blockchain, biotechnology, and robotics in enhancing sustainability, reducing waste, and improving food system efficiency. Consumer acceptance of engineered and fortified foods emerges as a critical factor in driving these innovations. The review also emphasizes the need for a transformative approach to food production, incorporating innovative technologies and sustainable practices to ensure food security by 2050. A coordinated effort to integrate alternate food sources and advanced processing methods will be vital for achieving a secure and sustainable global food future.

Colonoscopy is vital for diagnosing colorectal cancer, but limitations in instrument dexterity and sensor feedback can affect safety and patient comfort. We propose a disposable soft robotic "add-on" that attaches to existing endoscopic tools, enhancing safety without requiring custom instruments or workflow changes. The robot features soft optical sensors for 3D shape detection and force monitoring. If excessive force is detected, soft actuators redistribute pressure. A graphical interface provides real-time force data alongside the endoscope camera view. Validation experiments show accurate 3D shape reconstruction (8.51% curvature error, 9.67% orientation error) and force estimation up to 6 N with 3.38% accuracy. In-vitro tests confirm effective force redistribution, while ex-vivo tests on a bovine colon demonstrate smooth integration with minimal impact on the user learning curve. In-vivo swine studies validate safety and feasibility, confirming compatibility with existing tools and minimal disruption to clinical workflows, ensuring an efficient colonoscopy experience.

Computational research tools have reached a level of maturity that enables efficient simulation of neural activity across diverse scales. Concurrently, experimental neuroscience is experiencing an unprecedented scale of data generation. Despite these advancements, our understanding of the precise mechanistic relationship between neural recordings and key aspects of neural activity remains insufficient, including which specific features of electrophysiological population dynamics (i.e., putative biomarkers) best reflect properties of the underlying microcircuit configuration. We present ncpi, an open-source Python toolbox that serves as an all-in-one solution, effectively integrating well-established methods for both forward and inverse modeling of extracellular signals based on single-neuron network model simulations. Our tool serves as a benchmarking resource for model-driven interpretation of electrophysiological data and the evaluation of candidate biomarkers that plausibly index changes in neural circuit parameters. Using mouse LFP data and human EEG recordings, we demonstrate the potential of ncpi to uncover imbalances in neural circuit parameters during brain development and in Alzheimer's Disease.

This study aims to predict IDH wt with TERTp-mut gliomas using multiparametric MRI sequences through a novel fusion model, while matching model classification metrics with patient risk stratification aids in crafting personalized diagnostic and prognosis evaluations.Preoperative T1CE and T2FLAIR sequences from 1185 glioma patients were analyzed. A MultiChannel_2.5D_DL model and a 2D DL model, both based on the cross-scale attention vision transformer (CrossFormer) neural network, along with a Radiomics model, were developed. These were integrated via ensemble learning into a stacking model. The MultiChannel_2.5D_DL model outperformed the 2D_DL and Radiomics models, with AUCs of 0.806-0.870. The stacking model achieved the highest AUC (0.855-0.904) across validation sets. Patients were stratified into high-risk and low-risk groups based on stacking model scores, with significant survival differences observed via Kaplan-Meier analysis and log-rank tests. The stacking model effectively identifies IDH wt TERTp-mutant gliomas and stratifies patient risk, aiding personalized prognosis.