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EMG-based learning approach for estimating wrist motion
Related publications (32)
Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...
Lausanne2024Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024Bayes-optimal Learning of Deep Random Networks of Extensive-width
Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
2023Distribution-on-distribution regression via optimal transport maps
Victor Panaretos, Laya Ghodrati
We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...
OXFORD UNIV PRESS2022Fast screening of covariates in population models empowered by machine learning
Jan Sickmann Hesthaven, Nadia Terranova
One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...
2021Travel Time Prediction for Congested Freeways With a Dynamic Linear Model
Nikolaos Geroliminis, Semin Kwak
Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021The association of basic and challenging motor capacity with mobility performance and falls in young seniors
Kamiar Aminian, Anisoara Ionescu
Background: Understanding the association between motor capacity (MC) (what people can do in a standardized environment), mobility performance (MP) (what people actually do in real-life) and falls is important for early detection of and counteracting on fu ...
ELSEVIER IRELAND LTD2020Age-Dependent Asymmetry of Wrist Position Sense Is Not Influenced by Stochastic Tactile Stimulation
Jamie Paik, Olivier Lambercy, Harshal Arun Sonar
Stochastic stimulation has been shown to improve movement, balance, the sense of touch, and may also improve position sense. This stimulation can be non-invasive and may be a simple technology to enhance proprioception. In this study, we investigated wheth ...
2020A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression
Anthony Christopher Davison, Jörg Hager, Hélène Ruffieux
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...
INST MATHEMATICAL STATISTICS2020Bayesian modeling of brain function and behavior
The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...
EPFL2020