Study uses wearable sensors to predict infant motor development status.
problem Early detection of infant motor development status in high-risk infants.
method Machine learning classification algorithms using day-long inertial movement data from wearable sensors.
result Machine learning can predict infant motor development status from day-long movement data.
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about…
Matrix completion method for analyzing longitudinal data.
problem Sparse and irregular longitudinal data in clinical and biomedical research.
method Iterative Singular Value Decomposition (SVD) for low-rank approximation.
result Approximates individual progression curves, explains 30% of variability.
Machine learning models adapt to motor learning but face challenges.
problem Adapting machine learning to handle motor variability and differentiate new movements from known ones.
method Parameter adaptation, transfer and meta-learning, reinforcement learning.
result Challenges in applying machine learning models for motor learning support systems.
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
New algorithms mitigate impairment effect in stochastic bandits.
problem Impairment effect in stochastic multi-armed bandits.
method Developed two novel algorithms with bucketing to address impairment and temporal constraints.
result Achieved sublinear regret, explicitly capturing impairment cost.
Machine learning monitors detect motor overheating, adapting to concept drift.
problem Early detection of motor overheating in ships' propulsion systems.
method Machine learning and statistical methods using historical data to adapt to concept drift.
result The proposed monitors provide early detection of overheating during and after concept drifts.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
DNNs improve localization from channel estimates, overcoming practical impairments.
problem Improving localization accuracy from channel estimates in Massive MIMO systems.
method Principled feature design for DNNs invariant to practical impairments.
result DNN achieves high localization accuracy and generalization capability.
Geometry-aware models improve cross-subject EEG decoding accuracy.
problem Strong inter-subject variability in motor imagery decoding.
method Discriminative Congruence Transform (DCT), Deep Linear DCT (DLDCT), Deep DCT-UNet (DDCT-UNet).
result Improves transductive cross-subject accuracy by 2-3%.
Mobile app uses CNN to help visually impaired cross streets.
problem Helping visually impaired cross streets safely.
method LytNetV2 deep learning model for pedestrian traffic light detection.
result 96% classification accuracy and 6.15 degree angle error.
In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to …
Light neural network detects modulation in noisy signals.
problem Efficiently detecting modulation in noisy signals.
method Light neural network architecture invariant to impairments.
result Network achieves accuracy under realistic impairments.
Model forecasts motor vehicle collision rates with high accuracy.
problem Forecasting motor vehicle collision rates with high accuracy.
method Adopted Heston Stochastic Volatility model and extended it to account for seasonality and accelerated safety periods.
result Short-term forecasts show high accuracy (over 95%) and outperform existing models.
Enhanced EEG classification improves motor imagery detection with less computation.
problem Improving classification accuracy of motor imagery EEG signals.
method Integrates Block-Toeplitz structure into augmented covariance matrices and uses Siegel metric.
result Significantly reduces computational time without compromising classification accuracy.
Autoencoder shapes QAM symbols for improved GMI performance.
problem Improving Geometric Mean Information (GMI) in QAM constellation shapes.
method End-to-end learning of bit mappings using autoencoders.
result Achieved up to 0.2 bits/QAM symbol gain in GMI.
This study evaluates machine learning models for precise temperature estimation in PMSMs.
problem Precise temperature monitoring of PMSMs for automotive applications.
method Evaluation of various machine learning models (ordinary and weighted least squares, support vector regression, k-nearest neighbors, randomized trees, neural networks) on collected data.
result ML models can predict magnet temperature profiles as accurately as classical models, but differ in model size and efficiency.
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of t…
Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable. The development of a practical BCI posed the challenge of classifying single-trial EEG with a small training set. In this letter, we addressed this problem b…
Method predicts motor symptoms of Parkinson's disease from daily activities.
problem Objective monitoring of Parkinson's disease symptoms.
method Multi-layer Gaussian process models for three types of movement abnormalities.
result Strong agreement between model predictions and clinical annotations.
MANGA transfers policies across environments with varying dynamics and noise.
problem Transferring policies across multiple environments with different dynamics and motor noise.
method Decouples policy learning from system identification, trains dynamics-conditioned policies, and learns dynamics parameters from rollouts.
result Demonstrates effective transfer of learned policies across four MuJoCo agents using agnostic RL and imitation learning methods.
This study evaluates handwriting features to diagnose Parkinson's disease.
problem Diagnosing Parkinson's disease through handwriting analysis.
method Kinematic, geometrical, and non-linear features were evaluated using K-nearest neighbors, support vector machines, and random forest classifiers.
result Up to 93.1% accuracy in classifying Parkinson's disease and healthy subjects.
Paper learns versatile balancing and recovery motions for humanoid robots.
problem Training humanoid robots to handle unexpected perturbations.
method Hierarchical Deep Reinforcement Learning in a physics simulator.
result Learned skills comparable to preprogrammed controllers but more adaptable.
This paper improves MI-based BCIs by applying transfer learning across all components.
problem Reducing calibration effort for new subjects in MI-based BCIs.
method Proposes TL in spatial filtering, feature engineering, and classification blocks, and adds data alignment.
result Integrating data alignment and sophisticated TL significantly improves classification performance and reduces calibration effort.
Grasping is a complex process involving knowledge of the object, the surroundings, and of oneself. While humans are able to integrate and process all of the sensory information required for performing this task, equipping machines with this capability is an extremely challenging endeavor. In this paper, we investigate …
DyEnsemble improves BCI accuracy by adapting to nonstationary neural signals.
problem Nonstationary neural signals in BCI cause decoding errors.
method Dynamic ensemble modeling that learns and combines diverse models online.
result DyEnsemble outperforms Kalman filters, especially with noisy signals.
A decentralized deep RL controller improves hexapod locomotion learning.
problem Deep RL struggles with real-world legged robot control.
method Decentralized deep RL on a hexapod robot.
result Decentralized approach learns better and faster.
Study improves motor insurance claim prediction using geographic data.
problem Limited location identifiers in public actuarial datasets.
method Zone-level modeling framework with environmental and orthoimagery data.
result Geographic information improves MTPL claim prediction accuracy.
Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines E…
Study uses ANN with LM for ASD screening.
problem Lack of kinematic measures for ASD diagnosis.
method Artificial Neural Network (ANN) with Levenberg-Marquardt algorithm.
result Predictive accuracy of ANN for ASD detection.
Robotics learns new skills faster by reusing past movements.
problem Learning new motor skills is time-consuming and requires exploration of a large space of motor configurations.
method Combines probabilistic movement primitives with relative entropy policy search for skill initialization and adaptation.
result Quality of learned skills improves and the number of required iterations to learn a new task can be reduced by more than 60%.
Proposes a framework for learning constrained motor skills.
problem Learning constrained motor skills in robotic systems.
method Exploits probabilistic properties of multiple demonstrations in a linearly constrained optimization problem.
result Proposes a non-parametric solution for constrained motor skills.
Model improves robustness of neural network sequences without transition failures.
problem Learning and generating complex sequences of motor primitives without interference.
method Inspired by thalamocortical circuit, uses specific module for motif transitions.
result Improved robustness of sequence generation with no transition failures.
The study reveals fundamental limits of fraud detection in card payment networks.
problem Fraud detection in card payment networks is challenging due to structural information impairments.
method Formalized card authorization as a sequential decision problem with delayed feedback, derived minimax regret lower bound.
result Improving issuer reporting quality or reducing censorship can yield larger reductions in the regret floor than increasing model complexity.
Paper proposes RL algorithms for optimizing cellular network performance.
problem Improving cellular network performance against wireless impairments.
method Formulated as a reinforcement learning problem and developed two algorithms: PC and SON.
result RL algorithms outperform industry standards in simulated environments.
Deep learning detects Parkinson's disease severity from wearable data.
problem Measuring Parkinson's disease severity from accessible biomarkers.
method Developed and evaluated deep learning models on sensor data from wearable devices.
result Deep learning models outperform classical machine learning models in classifying Parkinson's disease severity.
Study shows insurance industry in North Macedonia declined 10% due to COVID-19.
problem Impact of COVID-19 on insurance industry activity.
method Seasonal autoregressive models and data analysis for 11 insurance classes.
result Insurance activity in North Macedonia decreased by more than 10% during the pandemic.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine learning.
Model detects insurance fraud using social network analysis.
problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.
This research improves EEG-based MI-BCI systems to be more resilient to emotional arousal.
problem Lack of robustness in EEG-based MI-BCI systems due to emotional arousal.
method Subjects were exposed to VR environments to induce high and low arousal states. Machine learning models were trained on proxy subjects instead of arousal states. MI models were trained for each subject.
result MI-BCI systems are made more resilient to emotional perturbations.
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
problem Predicting when ALS patients will experience significant functional decline.
method Multi-event survival analysis, covariate-based models.
result Covariate-based models outperform Kaplan-Meier estimator in predicting time-to-event outcomes.
Study shows statistical biases can mislead transformer models, impairing their generalization.
problem Statistical biases in transformers affect their ability to generalize.
method Evaluated transformer models on synthetic algorithmic tasks with varying statistical biases.
result Statistical biases lead to overestimation of transformer models' generalization capabilities.
The Giroux correspondence and the notion of a near force-free magnetic field are used to topologically characterize near force-free magnetic fields which describe a variety of physical processes, including plasma equilibrium. As a byproduct, the topological characterization of force-free magnetic fields associated with…
Paper proposes a statistical model for detecting mu-suppression in EEG signals.
problem Detecting mu-suppression in motor imagery EEG signals.
method Proposes a statistical model based on the generalized extreme value distribution (GEV) and a linear classifier.
result Preliminary results show good classification accuracy in detecting mu-suppression and distinguishing EEG events.
DIM shows promise in predicting Alzheimer's progression.
problem Predicting Alzheimer's disease progression from brain imaging.
method Used variants of Deep InfoMax (DIM) for brain imaging analysis.
result DIM outperforms supervised AlexNet and ResNet in Alzheimer's progression prediction.
Robot learns tool use from effects, detecting features of tools, objects, and actions.
problem Teaching robots to understand and manipulate objects using tools.
method Deep learning model trained on sensory-motor data from a robot performing a tool-use task.
result Robot can detect features of tools, objects, and actions from effects of object manipulation.
Study aims to measure and mitigate biases in motor insurance pricing.
problem Ethical biases in motor insurance pricing that affect fairness and regulatory compliance.
method Statistical methodologies and data analysis to measure and mitigate biases.
result Developed tools to measure and mitigate ethical biases in motor insurance pricing.