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Fatigue

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Bio-signals Collecting System for Fatigue Level Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fatigue is a risk factor that reduces quality of life and work efficiency, and threatens safety in a high-risk environment. However, fatigue is not yet precisely defined and is not a quantified concept as it relies on subjective evaluation. The purpo...

Non-invasive load monitoring based on deep learning to identify unknown loads.

PloS one
With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating sci...

Using Natural Language Processing to Extract and Classify Symptoms Among Patients with Thyroid Dysfunction.

Studies in health technology and informatics
In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these ...

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Archives of physical medicine and rehabilitation
OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

Medical & biological engineering & computing
In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on tradi...

Robotic assisted and exoskeleton gait training effect in mental health and fatigue of multiple sclerosis patients. A systematic review and a meta-analysis.

Disability and rehabilitation
PURPOSE: Robotic and Exoskeleton Assisted Gait Training (REAGT) has become the mainstream gait training module. Studies are investigating the psychosocial effects of REAGT mostly as secondary outcomes. Our systematic review and meta-analysis aims to ...

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Medical & biological engineering & computing
Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rat...

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

Scientific reports
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and bio...

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

Journal of neural engineering
. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not...