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3D force sensing is crucial for improving human–machine interactions and optimizing sports training. Current solutions typically rely on multisensor fusion and algorithmic decoupling, which are prone to signal crosstalk and limited angular resolution. Here, we introduce a concentric rheostat sensor that intrinsically decouples normal and shear forces using a hybrid capacitive–resistive sensing mechanism. The concentric contact resistor directly converts shear force directions into resistance values, achieving high‐resolution angular detection without extensive computational postprocessing. Fabricated via high‐resolution 3D printing of hydrogel, the sensor array is integrated into a smart table tennis paddle for real‐time acquisition of 3D force and impact location during ball strikes. The collected data is processed by a two‐stage machine learning pipeline. In the first stage, multiple linear regression (MLR) extracts robust raw metrics from primitive physical features. Subsequently, a random forest regressor evaluates these metrics to provide personalized performance grades that account for skill interdependence, including stability. This combination of intrinsic 3D force decoupling and data‐driven skill assessment demonstrates a compact, high‐precision platform for intelligent sports training and real‐time skill evaluation. A 3D‐printed sensor array intrinsically decouples normal and shear forces through a unique concentric structural design. By integrating piezoresistive, sliding area‐varying capacitive, and concentric rheostat mechanisms, the 12‐sensor module achieves high‐resolution 3D force mapping without complex algorithms. Demonstrated on a smart table tennis paddle, this robust platform enables real‐time biomechanical feedback and AI‐driven skill assessment in sports training.

PurposeA profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills for minimally invasive or robot-assisted procedures. This includes the objective and preferably automatic assessment of surgical skill. Recent studies presented good results for automatic, objective skill evaluation by collecting and analyzing motion data such as trajectories of surgical instruments. However, obtaining the motion data generally requires additional equipment for instrument tracking or the availability of a robotic surgery system to capture kinematic data. In contrast, we investigate a method for automatic, objective skill assessment that requires video data only. This has the advantage that video can be collected effortlessly during minimally invasive and robot-assisted training scenarios.MethodsOur method builds on recent advances in deep learning-based video classification. Specifically, we propose to use an inflated 3D ConvNet to classify snippets, i.e., stacks of a few consecutive frames, extracted from surgical video. The network is extended into a temporal segment network during training.ResultsWe evaluate the method on the publicly available JIGSAWS dataset, which consists of recordings of basic robot-assisted surgery tasks performed on a dry lab bench-top model. Our approach achieves high skill classification accuracies ranging from 95.1 to 100.0%.ConclusionsOur results demonstrate the feasibility of deep learning-based assessment of technical skill from surgical video. Notably, the 3D ConvNet is able to learn meaningful patterns directly from the data, alleviating the need for manual feature engineering. Further evaluation will require more annotated data for training and testing.

The study investigates the gender differences in information literacy skills among research scholars of Alagappa University. We collected 430 samples was from a population of 812 using simple random sampling. The questionnaire designed based on Big6 Skills and data were collected by using structured questionnaire. Descriptive statistics of frequency counts, simple percentages, mean, standard deviation and Independent 't' Test were used for data analysis. The analysis found that \"Information task definition skills is 4.26 with S.D. of 0.18\", \"Information seeking strategy skills is 4.02 with S.D. of 0.32\", and \"Location and Access Skills is 3.82 with S.D. of 0.386\". \"Information Use Skills is 4.02 with S.D. 0.184\", \"Synthesis Skills is 3.94 with S.D. of 0.317\", \"Fair Use of Information Skills is 3.98 with S.D. of 0.205\", Skills for the Evaluation of Collected / Downloaded Information is 3.94 with S.D. of 0.271\". The study reveals that gender differences exist between male and female research scholars of Alagappa University as far as most of information literacy skills are concerned. Further, the study show that male professionals revealed a slightly higher mean score in their Information Literacy skills. The paper suggests that some special programmes/workshops on information literacy skills are concerned. The university with the help of UGC may develop Indian Standards of Information Literacy skills while many developed countries have their own information literacy standards.

BackgroundEvaluation of robotic surgical skill has become increasingly important as robotic approaches to common surgeries become more widely utilized. However, evaluation of these currently lacks standardization. In this paper, we aimed to review the literature on robotic surgical skill evaluation.MethodsA review of literature on robotic surgical skill evaluation was performed and representative literature presented over the past ten years.ResultsThe study of reliability and validity in robotic surgical evaluation shows two main assessment categories: manual and automatic. Manual assessments have been shown to be valid but typically are time consuming and costly. Automatic evaluation and simulation are similarly valid and simpler to implement. Initial reports on evaluation of skill using artificial intelligence platforms show validity. Few data on evaluation methods of surgical skill connect directly to patient outcomes.ConclusionAs evaluation in surgery begins to incorporate robotic skills, a simultaneous shift from manual to automatic evaluation may occur given the ease of implementation of these technologies. Robotic platforms offer the unique benefit of providing more objective data streams including kinematic data which allows for precise instrument tracking in the operative field. Such data streams will likely incrementally be implemented in performance evaluations. Similarly, with advances in artificial intelligence, machine evaluation of human technical skill will likely form the next wave of surgical evaluation.

PurposeWith the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge.MethodsWe propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels.ResultsWe perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved competitive accuracies of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within a 1–3 second window, without needing an observation of entire training trial.ConclusionThis study highlights the potential of deep architectures for efficient online skill assessment in modern surgical training.

Abstract BackgroundDue to varied surgical skills and the lack of an efficient rating system, we developed Surgesture based on elementary functional surgical gestures performed by surgeons, which could serve as objective metrics to evaluate surgical performance in laparoscopic cholecystectomy (LC).MethodsWe defined 14 LC basic Surgestures. Four surgeons annotated Surgestures among LC videos performed by experts and novices. The counts, durations, average action time, and dissection/exposure ratio (D/E ratio) of LC Surgestures were compared. The phase of mobilizing hepatocystic triangle (MHT) was extracted for skill assessment by three professors using a modified Global Operative Assessment of Laparoscopic Skills (mGOALS).ResultsThe novice operation time was significantly longer than the expert operation time (58.12 ± 19.23 min vs. 26.66 ± 8.00 min, P < 0.001), particularly during MHT phase. Novices had significantly more Surgestures than experts in both hands (P < 0.05). The left hand and inefficient Surgesture of novices were dramatically more than those of experts (P < 0.05). The experts demonstrated a significantly higher D/E ratio of duration than novices (0.79 ± 0.37 vs. 2.84 ± 1.98, P < 0.001). The counts and time pattern map of LC Surgestures during MHT demonstrated that novices tended to complete LC with more types of Surgestures and spent more time exposing the surgical scene. The performance metrics of LC Surgesture had significant but weak associations with each aspect of mGOALS.ConclusionThe newly constructed Surgestures could serve as accessible and quantifiable metrics for demonstrating the operative pattern and distinguishing surgeons with various skills. The association between Surgestures and Global Rating Scale laid the foundation for establishing a bridge to automated objective surgical skill evaluation.

Learning Health Systems (LHSs) depend on the ability to effectively evaluate programs and translate data into practice. Within the Veterans Affairs (VA) health system, formal mandates require programmatic decision-making to be data-driven; however, opportunities for staff to develop applied program evaluation skills that support LHS learning cycles remain limited. To address this need, the synchronous virtual Evaluation Bootcamp Training (EBcT) was developed to equip learners with practical, applied evaluation skills. This manuscript describes the design rationale, structure, and core concepts of the training, emphasizing design decisions intended to support evaluation capacity building within a large and complex health system. EBcT was delivered over 4 days across 2 training cohorts (8 learners in 4 project teams from Cohort 1; 12 learners in 5 project teams from Cohort 2). Project teams addressed a range of clinical and operational initiatives within VA. Learners represented diverse professional roles, including lead evaluators, clinical subject matter experts, project managers, operational leaders, and analysts. Learners reported high satisfaction throughout the training, with full attendance at application sessions and strong participation in didactic components. A majority reported meeting predefined evaluation learning targets at the end of the training. Findings suggest that structured, team-based training models such as EBcT can strengthen evaluation capacity and support LHS functioning by equipping project teams to engage in repeated cycles of measurement, learning, and performance improvement.

Seasonal precipitation and temperature predictions with global climate models that are developed based on the ocean–atmosphere interactions, contribute to the water resources management and hazard mitigation. To date, multi-model ensemble seasonal climate prediction systems, such as North American multi-model ensemble (NMME), are an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean–atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, whether the NMME real-time forecasts perform as well as the hindcasts and how the decadal variability in ocean-atmospheric teleconnections affect the prediction skill remain unclear. In this paper, based on precipitation and temperature datasets from nine models of NMME, the evaluation of forecast skills during hindcast (1982–2010) and real-time forecast (2011–2020) periods is carried out in the Yangtze River basin over China. Results show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14–51% (38–75%) and error increased by 30–31% (51–55%) for seasonal precipitation (temperature) predictions up to the sixth lead-season, and the skill decrease is larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that a better grasp of decadal variability is needed to improve the real-time climate predictions.

Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This study evaluates the effectiveness of an artificial intelligence (AI)-based video analysis model in assessing microsurgical performance and examines the correlation between AI-derived parameters and specific surgical skill components. A dual AI framework was developed, integrating a semantic segmentation model for artificial blood vessel analysis with an instrument tip-tracking algorithm. These models quantified dynamic vessel area fluctuation, tissue deformation error count, instrument path distance, and normalized jerk index during a single-stitch end-to-side anastomosis task performed by 14 surgeons with varying experience levels. The AI-derived parameters were validated against traditional criteria-based rating scales assessing instrument handling, tissue respect, efficiency, suture handling, suturing technique, operation flow, and overall performance. Rating scale scores correlated with microsurgical experience, exhibiting a bimodal distribution that classified performance into good and poor groups. Video-based parameters showed strong correlations with various skill categories. Receiver operating characteristic analysis demonstrated that combining these parameters improved the discrimination of microsurgical performance. The proposed method effectively captures technical microsurgical skills and can assess performance.

This paper proposes a novel incremental model for acquiring skills and using them in Intrinsically Motivated Reinforcement Learning (IMRL). In this model, the learning process is divided into two phases. In the first phase, the agent explores the environment and acquires task-independent skills by using different intrinsic motivation mechanisms. We present two intrinsic motivation factors for acquiring skills by detecting states that can lead to other states (being a cause) and by detecting states that help the agent to transition to a different region (discounted relative novelty). In the second phase, the agent evaluates the acquired skills to find suitable ones for accomplishing a specific task. Despite the importance of assessing task-independent skills to perform a task, the idea of evaluating skills and pruning them has not been considered in IMRL literature. In this article, two methods are presented for evaluating previously learned skills based on the value function of the assigned task. Using such a two-phase learning model and the skill evaluation capability helps the agent to acquire task-independent skills that can be transferred to other similar tasks. Experimental results in four domains show that the proposed method significantly increases learning speed.