A deep learning framework assesses physical rehabilitation exercises.
problem Lack of versatile, robust, and practical assessment methods for rehabilitation exercises.
method Deep learning framework with metrics, scoring functions, and neural networks.
result First implementation of deep neural networks for rehabilitation performance assessment.
Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
Deep learning identifies functional primitives in stroke rehabilitation.
problem Measuring the necessary dose of rehabilitation training for stroke recovery.
method Automatic identification of functional primitives using IMUs and deep learning.
result Convolutional neural network achieved 70% accuracy in primitive classification.
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic …
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…
This paper presents a study in task-oriented approach to stroke rehabilitation by controlling a haptic device via near-infrared spectroscopy-based brain-computer interface (BCI). The task is to command the haptic device to move in opposing directions of leftward and rightward movement. Our study consists of data acquis…
The paper tracks patient recovery using graphs of joint movement data.
problem Tracking individual patient recovery trajectories in physical therapy.
method Bayesian learning of Random Geometric Graphs from joint movement data.
result Optimal exercise routines can be recommended based on patient recovery data.
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spina…
Improved ML approach for quantifying stroke rehabilitation.
problem No tool to measure functional training after stroke.
method Refined machine learning algorithm and sensor configurations.
result Linear Discriminant Analysis (LDA) showed highest accuracy.
New model for insurance states using Markov jump processes with non-countable state space.
problem Modeling insurance states with non-countable state spaces.
method Developed a new Thiele's differential equation for continuous time rehabilitation rates.
result Allows for consistent calculation of reserves in disability insurance.
Deep learning improves automated detection of epileptic seizures.
problem Automated detection of epileptic seizures using traditional methods is limited.
method Deep learning techniques for feature extraction and classification.
result Deep learning enhances accuracy in diagnosing epileptic seizures.
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spa…
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with …
This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neural networks. The generative adversarial network structure is adopted, whereby a discriminative and a generative model are trained concurrent…
More than one billion people live in slums around the world. In some developing countries, slum residents make up for more than half of the population and lack reliable sanitation services, clean water, electricity, other basic services. Thus, slum rehabilitation and improvement is an important global challenge, and a …
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of …
The paper shows that certain learned representations are identifiable in function space.
problem Identifiability of learned representations in deep neural networks.
method Using recent advances in nonlinear ICA, the paper shows that a large family of discriminative models are identifiable in function space, up to a linear indeterminacy.
result Many models for representation learning are identifiable in function space, including text, images, and audio.
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to …
Paper proposes a smart neck-band for detecting neck postures using integrated kinematic and kinetic data.
problem Improper neck postures lead to musculoskeletal disorders requiring therapy and rehabilitation.
method Integrated use of kinematic and kinetic data with machine learning algorithms.
result 100% accuracy in predicting neck postures using the proposed platform.
Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and invol…
SOM-VQ tokenizes discrete models with semantic structure and navigable topology.
problem Lack of semantic structure in vector quantized representations limits interpretable human control.
method Combines vector quantization with Self-Organizing Maps to learn discrete codebooks with explicit topology.
result SOM-VQ produces more learnable token sequences and provides an explicit navigable geometry in code space.
New approach improves human activity recognition with wearables.
problem Improving human activity recognition with wearables.
method Exploiting latent relationships between multi-channel sensor modalities, data-agnostic augmentation, and a classification loss criterion.
result Achieves new state-of-the-art performance on four diverse activity recognition benchmarks.
Revisits Isomap, showing it constructs Euclidean representations of geodesic structure.
problem Nonlinear dimension reduction of manifold data.
method Revisits Isomap's rationale, clarifying its approach to constructing Euclidean representations of geodesic structure.
result Convexity is not required for shortest path distances to converge to Riemannian distances.
BCI system improves word selection efficiency using sequential best-arm identification.
problem Conventional non-adaptive BCI paradigms lead to a lengthy learning process.
method Casted as sequence of best-arm identification tasks in multi-armed bandits, using pre-trained LLMs and STTS algorithm.
result Substantial empirical improvement in word selection efficiency demonstrated.
Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of …
Paper introduces active Bayesian method for assessing black-box classifiers efficiently.
problem Need to assess performance of black-box classifiers reliably with limited labels.
method Develops inference strategies and proposes active Bayesian framework for efficient instance selection.
result Significant gains in performance assessment with fewer labels compared to traditional methods.
Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
Although compelling assessments have been examined in recent years, more studies are required to yield a better understanding of the several methods where assessment techniques significantly affect student learning process. Most of the educational research in this area does not consider demographics data, differing met…
Bayesian networks improve product risk assessment by handling uncertainty and causality.
problem Limited handling of uncertainty and inability to incorporate causal explanations in existing methods.
method Bayesian Networks (BNs) for improved systematic product risk assessment.
result BN approach provides more powerful and flexible risk assessments.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
Improved assessment of knee osteoarthritis using geodesic B-score.
problem Need for automatic, reader-independent measures of osteoarthritis clinical outcomes.
method Derive a geodesic B-score for Riemannian shape spaces, develop efficient algorithm for large shape populations.
result Geodesic B-score exhibits improved discrimination ability over Euclidean B-score.
The paper discusses safety assessment for AI systems, focusing on machine learning models.
problem Safety assessment of AI systems, especially machine learning models, in safety-related applications.
method Analyzed AI models as statistical models and proposed a new budget allocation for AI safety.
result Safety assessment of AI systems requires a new approach focusing on the model used, not just the system.
Study quantifies motion dynamics of ankle sprains using biosensor data.
problem Diagnosing chronic ankle instability (CAI) based on objective biomechanical measures.
method Developed a nonlinear subspace clustering method to learn motion patterns from multi-joint coordination.
result Classification accuracy of >70% on CAI vs. normal controls using leave-one-subject-out cross validation.
Unified framework for CVA sensitivities, hedging, and risk assessment.
problem Computing and managing Credit Value Adjustment (CVA) sensitivities and risks.
method Probabilistic machine learning and refined regression on simulated data, validated by Monte Carlo methods.
result Identification of optimal sensitivities for practical tasks like hedging and risk assessment.
Large corporate credit models may be adapted for small business risk assessment.
problem Limited data and lack of credit analysts for small businesses.
method Adapting large corporate credit risk models for small businesses.
result Adapted models can predict small business credit risk effectively.
TIDBD adapts step sizes online for better robotic predictions.
problem Choosing appropriate learning parameters for online prediction-learning.
method Temporal-Difference Incremental Delta-Bar-Delta (TIDBD) for step-size adaptation.
result TIDBD performs comparably to classic TD learning and detects sensor failures.
Bias and heterogeneity in peer assessment can lead to the issue of unfair scoring in the educational field. To deal with this problem, we propose a reference ranking method for an online peer assessment system using HodgeRank. Such a scheme provides instructors with an objective scoring reference based on mathematics.
DecoupleNets use neural networks to assess and select dependence models.
problem Assessing and selecting dependence models for multivariate data.
method Neural networks (DecoupleNets) transform data to uniformity, then assess and select models.
result DecoupleNets provide a novel, efficient method for dependence model assessment and selection.
C-FAR automates clustering assessment for neural tracking.
problem Manual assessment of clusters by humans is slow and impractical for large datasets.
method C-FAR uses automated feedback queries to select optimal clustering from multiple algorithms.
result C-FAR produces near-perfect clustering on simulated neural data.
R2DE assesses new exam questions quickly and accurately.
problem Assessing newly generated exam questions efficiently and accurately.
method R2DE uses NLP to estimate difficulty and discrimination of questions based on their text.
result R2DE can estimate question difficulty and discrimination quickly and accurately.
Automated fatigue assessment using ECG and actigraphy sensors.
problem Fatigue assessment based on self-reporting suffers from recall bias.
method Wearable sensing, machine learning, feature selection, self-attention model, consistency self-attention mechanism.
result Very promising results achieved in fatigue assessment.
Develops methods for AI self-assessment to improve trustworthiness.
problem Uncertainty in AI predictions and lack of trust in AI systems.
method Uncertainty estimation techniques considering practical impacts and costs.
result Guidelines for selecting and designing effective AI self-assessment methods.
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, …
Model uses smartphone data to assess MS trajectories.
problem Personalized longitudinal MS assessment.
method Imputation, generalized estimation equation, ensemble learning, fine-tuning.
result Promising model for predicting MS over time.
New method assesses financial and cyber risks under uncertainty.
problem Uncertainty in risk assessment for financial and cyber systems.
method Combines stochastic approximation and distorted mix method to compute worst case average value at risk.
result Efficient algorithm for tail uncertainty in multivariate distributions.
New method approximates CV for model assessment and selection.
problem Efficient model assessment and selection with large number of folds.
method Approximates expensive refitting with a single Newton step warm-started from full training set optimizer.
result Uniform non-asymptotic, deterministic model assessment guarantees for approximate CV.
Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
The paper assesses quality measures for machine learning models using cross-validation.
problem Evaluating the accuracy and robustness of quality measures for machine learning models.
method Cross-validation approach to estimate prediction error and quantify explained variation. Confidence bounds and local quality measures derived from residuals.
result The reliability and robustness of quality measures are assessed through numerical examples and confidence bounds.