Sensing Eating Events in Context: A Smartphone-Only Approach

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Systems and methods for monitoring penile tumescence are provided that overcome the drawbacks of previously known systems by providing a wearable formed of a flexible and elastic tube having a plurality of sensors disposed on or embedded within it, the wea ...

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

A smart wearable (SW) for collecting data on an integrity of a joint of a wearer, the wearable including a first frame (1) configured to be attached in proximity of the joint of the wearer, the frame (1) including an elastic sensor frame (3) having an open ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The terminology of neurological disorders encompasses a range of serious illnesses (e.g., epilepsy, Alzheimer's disease) leading to morbidity, disability, and stigma. Epilepsy alone affects over 50 million people worldwide, and these figures are rising as ...

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validat ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

The dataset supporting 'Fiber pumps for wearable fluidic systems', published in Science in March 2023. In this work, we present a fluidic pump in the form of a flexible and stretchable tube. These tubes - or 'fiber pumps' - can generate significant fluidic ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

The channel impulse response (CIR) of the wireless propagation channel is influenced by the surrounding environment and thus can be used to retrieve environmental information such as location, the presence of objects, and the speed of objects. In this work ...

Sensing Eating Events in Context: A Smartphone-Only Approach

Chattez avec Graph Search

Person Re-Identification and its Application to Multi-Object Tracking

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Tumescense monitoring system for diagnosing erectile dysfunction and methods of use

Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

A wearable to assess knee joint integrity using non-contact acoustic sensors

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Real-time Personalized Monitoring of Neurological Disorders on Wearable Systems

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Fair Decentralized Learning

Hardware-Software co-design Methodologies for Edge AI Optimization

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Research data supporting 'Fiber pumps for wearable fluidic systems'

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Electronic Excited States from Physically Constrained Machine Learning

Deep Learning Generalization with Limited and Noisy Labels

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Learning-based Hand Gesture Classification using Channel Impulse Response with UWB