A Multimodal Dataset for Automatic Edge-AI Cough Detection

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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 ...

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

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 ...

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 ...

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 ...

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 ...

Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces t ...

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measu ...

Today, wearable heart rate monitors are an integral part of runners' training sessions, with heart rate data routinely used to assess effort intensity and stress on the body. As athletes translate their physical capacity into performance on the field throu ...

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdim ...

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

Wearable devices are nowadays at the edge-front in both academic research as well as in industry, and several wearable devices have been already introduced in the market. One of the most recent advancements in wearable technologies for biosensing is in the ...

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can ...

Background: Physical activity and sleep are behavioral risk factors for cancer that may be influenced by environmental exposures, including built and natural environments. However, many studies in this area are limited by residence-based exposure assessmen ...

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this pap ...

Presented herein are devices for collecting and/or channeling a biofluid (e.g., sweat, tears, saliva) and detecting and/or quantifying one or more biomarkers in the biofluid. The biomarker(s) may include, for example, ions, salts thereof, hormones and/or s ...

The rapid development of wearable sensing technology exhibits unprecedented opportunities for artificial intelligence by establishing an interactive interface between the physical and the virtual worlds. Energy preservation and multi-functional integration ...

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applica ...

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Chattez avec Graph Search

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

A Multimodal Dataset for Automatic Edge-AI Cough Detection

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

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

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

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

Hardware-Software co-design Methodologies for Edge AI Optimization

Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors

Towards real-world biomechanical analysis of performance and functional capacity using wearable sensors

Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

Self-supervised Segmentation via Background Inpainting

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Microfluidics by Additive Manufacturing for Wearable Biosensors: A Review

Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate

Embedding Mobile Health Technology into the Nurses' Health Study 3 to Study Behavioral Risk Factors for Cancer

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices

Apparatus for non-invasive sensing of biomarkers in human sweat

Printed silk-fibroin-based triboelectric nanogenerators for multi-functional wearable sensing

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud