AIMC Topic: Wearable Electronic Devices

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Wearable device for axillary lymph node screening in breast cancer based on infrared thermography and artificial intelligence.

Breast cancer research : BCR
BACKGROUND: Breast cancer (BC) is the most prevalent cancer among women worldwide, and patients with metastasis to axillary lymph nodes (ALN) experience significantly lower survival rates. Current imaging-based screening methods often suffer from low...

Aging With Artificial Intelligence: How Technology Enhances Older Adults' Health and Independence.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: As the global population ages healthcare challenges are escalating. Frailty, a clinical syndrome characterized by decreased reserve and resilience to stressors, is critically linked to adverse health outcomes in older adults. However, art...

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Computers in biology and medicine
Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not monitor it frequently enough to prevent serious complications. This is in part because the traditional cuff-based method is inconvenient, uncomfortabl...

Developments in Digital Wearable in Heart Failure and the Rationale for the Design of TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple CPET Study.

Circulation. Heart failure
BACKGROUND: Heart failure (HF) is a highly prevalent condition characterized by exercise intolerance, an important metric for ambulatory prognostication. However, current methods to assess exercise capacity are often limited to tertiary HF centers, l...

A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices.

Computers in biology and medicine
Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease that severely impacts patients' mobility and quality of life. To minimize the risk of falls and injuries associated with FoG, it is crucial to develop highly accurate FoG detecti...

Automatic cough detection via a multi-sensor smart garment using machine learning.

Computers in biology and medicine
Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routin...

Exploring equine behavior: Wearable sensors data and explainable AI for enhanced classification.

Journal of equine veterinary science
Understanding equine behavior through advanced monitoring technologies is crucial for improving animal welfare, optimizing training strategies, and enabling early detection of health or stress-related issues. This study integrates wearable sensor dat...

A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The aim of this study is to extract a patient-specific viscosity equation from photoplethysmography (PPG) data. An aging society has increased the need for remote, non-invasive health monitoring systems. However, the circula...

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously buil...

Learning Sensor Sample-Reweighting for Dynamic Early-Exit Activity Recognition Via Meta Learning.

IEEE journal of biomedical and health informatics
During recent years, dynamic early-exit has provided a promising paradigm to improve the computational efficiency of deep neural networks by constructing multiple classifiers to let easy samples exit at shallow layers while avoiding redundant computa...