AIMC Topic: Wearable Electronic Devices

Clear Filters Showing 201 to 210 of 1054 articles

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring.

IEEE transactions on biomedical circuits and systems
Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunately, these models are not yet widely adopted in health monitoring due to exceptionally high requiremen...

CLUMM: Contrastive Learning for Unobtrusive Motion Monitoring.

Sensors (Basel, Switzerland)
Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. More recent approaches have employed unobtrusive, real-time sensing ...

Human sleep position classification using a lightweight model and acceleration data.

Sleep & breathing = Schlaf & Atmung
PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux dise...

Enhanced Sensitivity and Versatile Detection: Dual-Sized Microsphere-Type Pressure Sensors for Soft Robotics and Wearable Electronics.

ACS applied materials & interfaces
The development of pressure sensors with enhanced sensitivity, expanded working range, and versatile yet decoupling detection capabilities is critical for advancing robotics and medical applications. This work presents a novel pressure sensor design ...

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

IEEE transactions on neural networks and learning systems
This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called ...

Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities.

Scientific reports
Disabled persons demanding healthcare is a developing global occurrence. The support in longer-term care includes nursing, intricate medical, recovery, and social help services. The price is large, but advanced technologies can aid in decreasing expe...

Bioinspired Super-Robust Conductive Hydrogels for Machine Learning-Assisted Tactile Perception System.

Advanced materials (Deerfield Beach, Fla.)
Conductive hydrogels have attracted significant attention due to exceptional flexibility, electrochemical property, and biocompatibility. However, the low mechanical strength can compromise their stability under high stress, making the material susce...

Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.

Sensors (Basel, Switzerland)
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning fr...

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things.

PloS one
With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others in different environments, antenna play a pivotal role in such wearables. In this communication, a no...

Application of Wearable Insole Sensors in In-Place Running: Estimating Lower Limb Load Using Machine Learning.

Biosensors
Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practi...