Deep RNN detects FoG episodes in Parkinson's disease with high accuracy.
problem Detecting freezing episodes in Parkinson's disease patients.
method Deep Recurrent Neural Network (RNN) with Long Short-Term Memory cells on 3D-accelerometer measurements.
result Frequency domain features from trunk sensor achieve an AUC score of 93% in subject-independent method.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used. In most approaches, it is necessary to introduce prior knowledge on the gaits to limit the highly non-convex search space of the policies. In this work, we propose a new approach to encode the symme…
Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes …
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concen…
Study investigates XAI methods in clinical gait analysis.
problem Limited understanding of machine learning models in healthcare.
method XAI methods, specifically Layer-wise Relevance Propagation (LRP), to explain ML predictions.
result Explanations from LRP show promising statistical and clinical relevance.
Machine learning improves accuracy of running gait event detection from tibial acceleration.
problem Accurate detection of running gait events from tibial acceleration data.
method Structured machine learning models compared to heuristic methods.
result Structured recurrent neural network model offers most accurate estimation of gait events.
Study freezing sets for digital images in a 2D grid.
problem Determine minimal freezing sets for digital images.
method Prove methods to obtain freezing sets for digital images (X, c_i) where X is a subset of Z^2.
result Examples show how methods can lead to the determination of minimal freezing sets.
Study minimal freezing sets in convex digital disks.
problem Finding minimal freezing sets in convex digital disks.
method Showed how to find minimal freezing sets for convex disks in digital plane.
result Found minimal freezing sets for convex disks in digital plane.
New method for freezing sets in arbitrary dimensions.
problem Creating freezing sets for digital images in arbitrary dimensions.
method Using c1 and cn adjacencies to obtain freezing sets in Zn. result Demonstrated freezing sets for digital images in arbitrary dimensions.
Study on cold and freezing sets in digital images.
problem Properties of cold sets in digital images.
method Analysis of properties and relationships between cold and freezing sets.
result Examined relationships between cold and freezing sets.
Paper predicts TUG score from gait characteristics using machine learning.
problem Assessing fall risk in the elderly.
method 3D pose estimation, gait characteristics computation, copula entropy selection, predictive models.
result Effectiveness of the proposed method demonstrated on real-world data.
Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems …
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely pro…
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.
GAIT-prop derives a biologically plausible learning rule from backpropagation.
problem Biological implausibility in traditional backpropagation for neural networks.
method GAIT-prop uses a top-down model to convert output error into plausible targets for weight updates.
result GAIT-prop and backpropagation give identical weight updates under certain conditions.
We introduce a local move on a link diagram named a region freeze crossing change which is close to a region crossing change, but not the same. We study similarity and difference between region crossing change and region freeze crossing change.
New tools for constructing fixed point sets in digital topology.
problem Constructing fixed point sets in digital topology.
method Defining excludable points and articulation points, and showing their exclusion from freezing sets.
result Excludable points and articulation points can be excluded from all freezing sets.
This study compares different data preprocessing methods for gait analysis.
problem Improving the generalizability of machine learning models in gait analysis.
method Compared various data preprocessing steps including filtering, time derivative, normalization, and scaling.
result Different preprocessing combinations affect gait classification performance.
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alter…
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a t…
Examines how irreducibility and rigidity affect digital images.
problem Understanding interactions between irreducibility and rigidity in digital images.
method Analyzes Cartesian products, wedges, and cold and freezing sets.
result Interactions between irreducibility and rigidity in digital images.
New gait segmentation method identifies users and adversaries with high accuracy.
problem Simultaneous identification of users and adversaries from accelerometer data.
method Geometric features and a new similarity metric for time series analysis.
result 98.79% accuracy for 6 classes (user-adversary identification) and 99.06% for binary (user only identification).
Survey on reducing deep neural network training complexity.
problem Efficient training of large deep neural networks.
method Pruning and freezing parts of the network during training.
result Dimensionality reduction improves training efficiency.
Find limiting sets for digital cones and suspensions.
problem Digital topology cone and suspension constructions.
method Identify (m, n)-limiting sets, especially (0, 0)-freezing sets.
result Discover (0, 0)-limiting sets for digital cones and suspensions.
Improved off-policy selection and learning in contextual bandits with better guarantees.
problem Selecting or training a reward-maximizing policy using data from a fixed behavior policy.
method A betting-based confidence bound applied to an inverse propensity weight sequence for off-policy selection, and a freezing condition for off-policy learning.
result The proposed methods achieve significantly improved guarantees over prior work, especially in small-data regimes.
Pre-trained model from healthy ADLs improves gait pattern classification for Parkinson's disease.
problem Limited training data for deep learning models in healthcare applications.
method Used convolutional autoencoder to extract features from healthy ADLs data and trained a multi-layer perceptron model for Parkinson's disease classification.
result Features extracted from healthy ADLs data can be used to train an effective classification model for Parkinson's disease.
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…
Simple fine-tuning of GANs with frozen discriminator outperforms existing methods.
problem Fine-tuning GANs requires numerous data and heavy computation.
method Freeze lower layers of the discriminator for fine-tuning.
result FreezeD significantly outperforms previous techniques on various datasets.
Freezing intermediate layers reduces DNN inference latency.
problem High latency in deep learning inference due to computationally expensive models.
method Introduce approximate caching at each intermediate layer to avoid running all layers for many inference requests.
result Reduces the number of effective layers by half for 91.58% of CIFAR-10 requests.
The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empiri…
Machine learning classifies Parkinson's Disease stages from walker sensors data.
problem Limited cost-effective methods for quantitatively assessing Parkinson's Disease stages.
method Machine learning applied to walker-mounted sensors data, feature selection methods compared.
result Feature selection method using ANOVA provides similar accuracy to full feature set and is clinically interpretable.
Model shows bailout stigma affects firm funding and market performance.
problem Bailout stigma impacts firm funding and market stability.
method Developed a model to analyze the effects of bailout stigma on firms and markets.
result Firms avoid stigma by withdrawing or refusing bailouts, leading to market freeze or revival.
PerCDL learns personalized dictionaries for physiological signals combining global and local structures.
problem Representing datasets with both global and local structures in human physiological signals.
method Personalized Convolutional Dictionary Learning (PerCDL) that combines a global and personalized local dictionary.
result PerCDL effectively learns interpretable representations for human locomotion data.
We study the effect of liquidity freezes on an economic agent optimizing her utility of consumption in a perturbed Black-Scholes-Merton model. The single risky asset follows a geometric Brownian motion but is subject to liquidity shocks, during which no trading is possible and stock dynamics are modified. The liquidity…
Bayesian model predicts circular data with fast Gibbs sampling.
problem Predicting circular data in scientific fields.
method Expressive von Mises quasi-processes with Stratonovich augmentation for posterior inference.
result Fast Gibbs sampling for posterior inference.
Enhances swaption modeling with rough stochastic volatility.
problem Modeling swaption volatility in post-LIBOR markets.
method Introduces rough stochastic volatility into FMM and rigorously justifies the freezing approximation.
result Establishes a new framework connecting FMM to rough Bergomi for forward swap rates.
Paper introduces Auto DeepVis to explain catastrophic forgetting in continual learning.
problem Catastrophic forgetting in continual learning of deep neural networks.
method Auto DeepVis and critical freezing techniques to address catastrophic forgetting.
result Critical freezing outperforms other methods on both past and future tasks.
SOR framework improves transfer learning efficiency and adaptability.
problem Limited data and overfitting in transfer learning.
method Freezes network structures while using group lasso and L1 penalties. result Achieves competitive results on few-shot medical imaging tasks.
New system uses wearable bio-signals for easy authentication.
problem Security of private information on wearables is a concern.
method Context-dependent soft-biometric authentication using heart rate, gait, and breathing audio.
result Binary SVM with RBF kernel achieves high accuracy and low EER.
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.
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot…
We continue the work of [10], studying properties of digital images determined by fixed point invariants. We introduce pointed versions of invariants that were introduced in [10]. We introduce freezing sets and cold sets to show how the existence of a fixed point set for a continuous self-map restricts the map on the c…
Analyzes how financial network dependencies can lead to multiple equilibrium outcomes and optimal bailout strategies.
problem Multiple equilibrium outcomes in financial networks due to dependency cycles.
method Characterized necessary and sufficient conditions for bank solvency, and provided upper bounds on optimal bailout payments.
result Minimum bailout payments needed to ensure systemic solvency and prevent cascading defaults.
Study shows not all ML models are uniquely identifiable from data.
problem Identifiability issues in machine learning models.
method Investigated through a case study on gait dynamics using a bipedal-spring mass model.
result Some parameters can be identified, but others remain unidentifiable.
Modified RV-coefficient reveals how training affects neural network representations.
problem Understanding how training affects intermediate representations in convolutional neural networks.
method Experimented with modified RV-coefficient (RV2) to compare activation patterns in deep networks trained on varying amounts of data and layers.
result RV2 successfully recovered expected similarity patterns and provided interpretable similarity matrices.
Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. However, models learned on synthetic images often do not generalize well to real images due to the domain shift. Our key idea to improve domain …
Paper explores BERT's efficiency on SQuAD2.0, freezing layers and using adapters.
problem Improving BERT's efficiency for SQuAD2.0 while maintaining performance.
method Freezing transformer layers, using adapters, and context-aware convolutional filters.
result Context-aware convolutional filters do not improve practical efficiency.