New models disentangle body pose and appearance for flexible human body analysis.
problem Interpretable latent space for human body analysis.
method Conditional-DGPose and Semi-DGPose models for disentangled pose and appearance.
result Models enable independent manipulation of pose and appearance.
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
Machine learning shapes bodies via inverse scattering.
problem Generating 3D human bodies with customizable characteristics.
method Combining inverse scattering with machine learning.
result Generates geometric bodies from characteristic parameters.
Proposes using Dynamic Mode Decomposition with delays for short-term human motion anticipation.
problem Lack of interpretability and explainability in neural network-based motion anticipation methods.
method Dynamic Mode Decomposition with delays for motion representation and prediction.
result Anticipation errors comparable or better than recurrent neural networks for very short times.
This research predicts diabetes mellitus using machine learning techniques.
problem Early prediction of diabetes mellitus to control and save human life.
method Exploring various risk factors related to diabetes using four machine learning algorithms (SVM, NB, KNN, C4.5 Decision Tree) on adult population data.
result C4.5 decision tree achieved higher accuracy in predicting diabetic mellitus.
Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist to…
CNN improves salt body interpretation in seismic imaging.
problem Manual salt body interpretation is time-consuming and prone to bias.
method U-Net and ResNet with ELU activation and Lovász-Softmax loss.
result CNN predictions match manual interpretations well, especially in weak reflection areas.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural networ…
New benchmark for non-rigid 3D human shape retrieval.
problem Distinguishing between body shapes of 3D human models.
method Extended benchmark with 145 new models and FAUST dataset.
result Improved comparison of 25 shape retrieval methods.
TraLFM models human mobility patterns from traffic trajectories.
problem Understanding human mobility patterns from traffic data.
method Latent factor modeling of sequential, personal, and temporal factors.
result TraLFM significantly outperforms state-of-the-art methods in latent factor analysis and next location prediction.
Deep learning tracks body parts without markers, improving efficiency in neuroscience.
problem Efficiently tracking specific behaviors in animals without intrusive markers.
method Transfer learning with deep neural networks for markerless tracking.
result Deep learning achieves excellent tracking performance with minimal labeled data.
Paper generates high-resolution fashion images based on body pose.
problem Limited variety of outfit images for shopping.
method Generative model trained on body pose and style transfer.
result Realistic high-resolution images of models in custom outfits.
Study builds complex network from multimodal physiological data.
problem Understanding dynamic interactions in biological systems.
method Network-based multimodal data fusion using recurrence plots and temporal metrics.
result Model accurately characterizes emotional states through physiological responses.
In this paper we propose the time-dependent generalization of an `ordinary' autonomous human biomechanics, in which total mechanical + biochemical energy is not conserved. We introduce a general framework for time-dependent biomechanics in terms of jet manifolds associated to the extended musculo-skeletal configuration…
Study examines how body segments respond to random vibrations.
problem Understanding human body responses to random vibrations.
method 35 participants were tested with random noise signals. Multiple linear regression models were created to determine influential predictors of peak translational gains.
result Multiple predictors, including motion direction and body segment, significantly influence peak translational gains.
Study on discrete Okounkov bodies and their applications.
problem Understanding stability and thresholds in higher dimensions.
method Analysis of discrete Okounkov bodies and gap phenomena.
result Asymptotic analysis of stability and thresholds.
Neural networks can predict BMI from faces, and can be fooled into wrong predictions.
problem Vulnerability of BMI prediction models to adversarial attacks.
method Test-time adversarial attacks on neural networks trained to infer BMI from facial images.
result Neural networks can be tricked into predicting incorrect BMI values, posing a risk of insurance fraud.
Dataset of Bose-Einstein condensates images aids ML in many-body physics.
problem Understanding solitons in Bose-Einstein condensates.
method Machine learning (ML) framework with convolutional neural networks and physics-informed classifiers.
result Automatic labeling of solitonic excitations in experimental images.
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS…
Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features . In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challengin…
DMGNN predicts 3D human motions using adaptive multiscale graphs.
problem Predicting 3D skeleton-based human motions accurately.
method Dynamic multiscale graph neural networks (DMGNN) with adaptive multiscale graphs and MGCU.
result DMGNN outperforms state-of-the-art methods in short and long-term predictions.
Compactifications of N-body problems help in spectral theory and symmetry analysis.
problem Analyzing the N-body problems using compactifications.
method Study of a compactification MN of R3N compatible with N-body Hamiltonian HN. result Compactifications by Georgescu and Vasy coincide, providing insights into N-body Hamiltonians.
Sharp stability results for reverse isoperimetric inequalities in 2D.
problem Reverse isoperimetric inequalities in the plane.
method Stability analysis of λ-convex bodies and convex bodies with smooth boundaries. result Sharp stability results for reverse isoperimetric inequalities, including inradius and Cheeger inequalities.
New method for surface analysis using restricted deformation bases.
problem Surface registration and comparison without pre-registered data.
method Elastic Riemannian metrics with basis-restricted transformations.
result Effective implementation on human body and face scans.
MT-VAE learns motion transitions for generating diverse future motions.
problem Learning long-term human motion sequences with transitions.
method Jointly learns motion mode embeddings and transitions using Variational Auto-Encoders.
result Generates multiple plausible future motion sequences from input.
LLMs can simulate human investment attitudes based on personality traits.
problem Investigating how LLMs mimic human investment behaviors.
method Simulated investment task using LLM personas with specific Big Five personality profiles.
result LLMs can produce meaningful behavioural differences in investment tasks that align with human traits.
Paper proposes Roweisposes for 3D action recognition using generalized eigenvalue problem.
problem Need for basic methods in 3D action recognition.
method Roweisposes uses Roweis discriminant analysis for generalized subspace learning.
result Roweisposes is effective for 3D action recognition.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
problem Validating novel human-vehicle interfaces and driver assistance systems.
method Driving simulator with in-cabin mock-up and camera system.
result Validation of in-cabin monitoring functions for advanced driver assistance and automated driving.
CNNs classify human activities from IMU data.
problem Automatic identification of physical activities using motion sensors.
method Used Convolutional Neural Networks (CNNs) with raw IMU data.
result CNNs perform well in classifying 16 lower-limb activities.
The paper evaluates the importance of monotonicity in AI fairness across various fields.
problem Ensuring fairness in AI applications across criminology, education, health care, and finance.
method Theoretical reasoning, simulation, and extensive empirical analysis of monotonic neural additive models (MNAMs).
result Monotonicity is essential for fairness in AI ethics and society, especially in criminology, education, health care, and finance.
This thesis tackles learning reward functions from human comparative feedback.
problem Designing reward functions for complex tasks is challenging and humans often provide suboptimal demonstrations.
method Proposes learning reward functions from comparative feedback (pairwise comparisons, best-of-many choices, rankings, scaled comparisons) and active learning techniques.
result Demonstrates the effectiveness of learning reward functions from comparative feedback in various domains.
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This pape…
A novel method predicts shape development using Riemannian shape spaces.
problem Predicting future shape development from a single observation.
method Proposes a novel prediction method that encodes shapes in a Riemannian shape space and learns hierarchical statistical models.
result Outperforms deep learning-supported variants and state-of-the-art methods in predicting shape development.
Counterexamples show Marchal's lemma fails for certain N-body systems.
problem Understanding when Marchal's lemma for N-body collisions holds or fails.
method Using metric geometry and the Jacobi-Maupertuis reformulation of mechanics, the team created counterexamples.
result Counterexamples demonstrate Marchal's lemma does not always apply to N-body systems.
Study shows semi-supervised learning improves human activity recognition with minimal user input.
problem Improving human activity recognition models using incremental learning.
method Three approaches: non-supervised, semi-supervised, and supervised learning were compared.
result Semi-supervised learning achieves similar accuracy to supervised learning with minimal user input.
Exact solution found for two-body financial dealer model using kinetic theory.
problem Finding an exact solution for the two-body financial dealer model.
method Exact master-Liouville equations derived using kinetic theory and probability currents.
result Exact order-book profile and average transaction interval derived.
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Study geodesics on flat tori, focusing on convex bodies.
problem Analyze geodesics orthogonal to convex subsets on flat tori.
method Define anisotropic Sobolev spaces and study properties of geodesics.
result Compute residues of geometric Epstein function in terms of intrinsic volumes.
Automatic detection of reflective thinking in math problem solving using body movement data.
problem Detecting reflective thinking in children's mathematical problem solving.
method WeDraw-1 Movement Dataset, Long Short-Term Memory neural networks, end-to-end detection.
result Average F1 scores of 0.73 for automatic detection and 0.79 for end-to-end detection of reflective thinking.
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
Tree regularization makes deep models interpretable by approximating them with simple decision trees.
problem Lack of interpretability in deep neural networks.
method Tree regularization to train deep models to resemble compact, axis-aligned decision trees.
result Tree regularized models are easier for humans to interpret without sacrificing accuracy.
Complexity is an interdisciplinary concept which, first of all, addresses the question of how order emerges out of randomness. For many reasons matrices provide a very practical and powerful tool in approaching and quantifying the related characteristics. Based on several natural complex dynamical systems, like the str…
Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.
problem Undetected perinatal stroke leads to lifelong disability.
method Wearable accelerometers and Discriminative Pattern Discovery (DPD) for automated GMA.
result Automated method correctly identifies abnormal movements with human-level accuracy.
New sEMG dataset for ADL activities recognized with high accuracy.
problem Developing a robust dataset for sEMG-based ADL activity recognition.
method Acquired sEMG data from 25 subjects performing 22 ADL activities. Used 4 classifiers with various feature sets.
result SVM classifier achieved 83.21% accuracy on 5 FAABOS categories.
Deep learning explains individual gait patterns in clinical biomechanics.
problem Understanding complex gait patterns from medical data.
method Layer-Wise Relevance Propagation (LRP) technique to attribute relevance of input variables to model predictions.
result Demonstrates which input variables are most relevant for characterizing individual gait patterns.
System detects and predicts cardiac anomalies from ECG data.
problem Early detection of cardiac anomalies to reduce healthcare costs and mortality.
method Discrete Wavelet Transform (DWT) and Undecimated Wavelet Transform (UWT) for feature extraction; Bayesian Network Classifier for anomaly prediction.
result Average accuracy of 96.6% for PAC, 92.8% for MI, and 87% for PVC on real ECG datasets.
Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determin…