arXiv research

New approach uses graphs for sign language recognition.

mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.

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…

ES-VAE models skeletal pose trajectories by removing nuisance factors.

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 derived from the extended musculo-skeletal configuration manifold. The …

Skeletal signatures were introduced in [J W Anderson and A Wootton, A Lower Bound for the Number of Group Actions on a Compact Riemann Surface, Algebr. Geom. Topol. 12 (2012) 19--35.] as a tool to describe the space of all signatures with which a group can act on a surface of genus $σ\geq 2$. In the present paper we pr…

Wearable smart suit tracks infant movements with high accuracy.

We consider a generic configuration of regions, consisting of a collection of distinct compact regions $\{Ω_i\}$ in $\mathbb{R}^{n+1}$ which may be either smooth regions disjoint from the others or regions which meet on their piecewise smooth boundaries $\mathcal{B}_i$ in a generic way. We introduce a skeletal linking …

New method recovers differential cohomology from diffeological spaces.

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…

Study develops sign recognition system for DHH users.

ProMoD models human race drivers with probabilistic movement primitives and neural networks.

Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.

In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous actio…

Paper reduces movement primitive dimensionality in parameter space.

This work creates a system for understanding human movement in spaces.

We prove that if $M$ is a CW-complex, then the homotopy type of the skeletal filtration of $M$ does not depend on the cell decomposition of $M$ up to wedge products with $n$-disks $D^n$, when the later are given their natural CW-decomposition with unique cells of order 0, $(n-1)$ and $n$; a result resembling J.H.C. Whi…

Graphs model human mobility patterns, reducing errors in data matching.

New homology theory for metric spaces, proving stability and anticipating topological changes.

Large-scale eye-tracking dataset for Atari games.

Paper proposes a deep learning approach for hand movement classification from EEG.

Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas. One of the key factors for intimate human-machine interaction is the ability of the control algorithm to detect and classify different user commands with shortest possible latency, thus ma…

Improved action recognition in live videos with hybrid FR-DL method.

We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response …

A deep learning framework assesses physical rehabilitation exercises.

TraLFM models human mobility patterns from traffic trajectories.

New privacy method for eye tracking data reduces correlations and maintains accuracy.

Smartwatch HRV measurements improved with machine learning.

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…

Machine learning models adapt to motor learning but face challenges.

Predict stock price movements using financial data and news articles with LLMs.

Novel singularity models for 4D harmonic forms and spinors from polytopes.

The paper models crime risk using Foursquare check-ins and mobility data.

Study shows context-specific models improve swipe gesture authentication for smartphone users.

We prove that the number of distinct group actions on compact Riemann surfaces of a fixed genus $σ\geq 2$ is at least quadratic in $σ$. We do this through the introduction of a coarse signature space, the space $\mathcal{K}_σ$ of {\em skeletal signatures} of group actions on compact Riemann surfaces of genus $σ$. We di…

In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space. So far, the development and application of GANs have been predominantly focused on spatial data such as images. In this project,…

Proposes a THGNN for dynamic financial time series prediction.

The paper presents a method to reduce arm motion complexity for prosthetics and robotics.

Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…

CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.

Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market move…

Improved crypto market forecasting using historical price reactions to tweets.

The space of graphs is often characterised by a non-trivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited f…

Method discovers user habits from mobile data.

In this article we describe a canonical way to expand a certain kind of $(\mathbb Z_2)^{n+1}$-colored regular graphs into closed $n$-manifolds by adding cells determined by the edge-colorings inductively. We show that every closed combinatorial $n$-manifold can be obtained in this way. When $n\leq 3$, we give simple eq…

This paper introduces a new clustering technique, called {\em dimensional clustering}, which clusters each data point by its latent {\em pointwise dimension}, which is a measure of the dimensionality of the data set local to that point. Pointwise dimension is invariant under a broad class of transformations. As a resul…

The applications of techniques from statistical (and classical) mechanics to model interesting problems in economics and finance has produced valuable results. The principal movement which has steered this research direction is known under the name of `econophysics'. In this paper, we illustrate and advance some of the…

Deep convolutional architecture identifies eye movements for biometric faster and more accurately.