AIMC Topic: Monitoring, Ambulatory
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Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 1, 2022
Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation s...
Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning.
Scientific reports
Sep 9, 2021
Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and so...
5G-enabled contactless multi-user presence and activity detection for independent assisted living.
Scientific reports
Sep 2, 2021
Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with...
Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.
Advanced materials (Deerfield Beach, Fla.)
Aug 31, 2021
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can...
Interpretation of Electrocardiogram Heartbeat by CNN and GRU.
Computational and mathematical methods in medicine
Aug 29, 2021
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the med...
Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.
PloS one
Jun 24, 2021
BACKGROUND: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values ca...
Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.
American heart journal
May 24, 2021
BACKGROUND: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.
Sensors (Basel, Switzerland)
Nov 13, 2020
Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We a...
Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting.
Epilepsia
Oct 11, 2020
OBJECTIVE: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessm...
Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.
Nature reviews. Cardiology
Oct 9, 2020
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in p...
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