AIMC Topic: Wearable Electronic Devices

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Towards Multiple Sclerosis Personalised Interventions Based on Real-World Predictive Analytics.

Studies in health technology and informatics
This study investigates the use of machine learning (ML) techniques to predict intervention response in patients with Multiple Sclerosis (PwMS) using real-world data from wearable devices. Data from 27 PwMS, monitored over two months were analyzed wi...

A stretchable, adhesive, and wearable hydrogel-based patches based on a bilayer PVA composite for online monitoring of sweat by artificial intelligence-assisted smartphones.

Talanta
Real-time monitoring of sweat using wearable devices faces challenges such as limited adhesion, mechanical flexibility, and accurate detection. In this work, we present a stretchable, adhesive, bilayer hydrogel-based patch designed for continuous mon...

Point-of-Care Testing: The Convergence of Innovation and Accessibility in Diagnostics.

Analytical chemistry
Over the years, the evolution of point-of-care testing (POCT) has been driven by technological advancements in materials, design, and artificial intelligence, as well as breakthrough developments in wearable technologies. These innovations are shifti...

Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications.

ACS nano
In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein de...

Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.

Biosensors
Urinary incontinence affects many women, yet there are no monitoring devices capable of accurately capturing flow dynamics during everyday activities. Building on our initial development of a wearable personal uroflowmeter, this study enhances the de...

Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities.

Sensors (Basel, Switzerland)
To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physio...

Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.

Scientific reports
Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance o...

Are Wearable ECG Devices Ready for Hospital at Home Application?

Sensors (Basel, Switzerland)
The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the r...

Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals.

Sensors (Basel, Switzerland)
Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a "snapshot in time" of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for co...

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.

Journal of medical Internet research
BACKGROUND: Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artifi...