Related papers: Sleep Quality Prediction from Wearables using Conv…

Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continuous, monitoring of daily sleep, and methods such as sleep…

In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge…

This work explores the problem of exercise quality measurement since it is essential for effective management of diseases like cerebral palsy (CP). This work examines the assessment of quality of large amplitude movement (LAM) exercises…

Precise monitoring of respiratory rate in premature infants is essential to initiate medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a Deep Learning enabled wearable monitoring…

Sleep stages pattern provides important clues in diagnosing the presence of sleep disorder. By analyzing sleep stages pattern and extracting its features from EEG, EOG, and EMG signals, we can classify sleep stages. This study presents a…

This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter…

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of…

Sleep deprivation is a public health concern that significantly impacts one's well-being and performance. Sleep is an intimate experience, and state-of-the-art sleep monitoring solutions are highly-personalized to individual users. With a…

Convolutional Neural Networks are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition, and segmentation. Recent research results demonstrate that…

Diagnosing sleep disorders is an important focus in neuroscience and engineering, as these conditions involve issues such as insufficient sleep, frequent awakenings, and difficulty reaching deep sleep. Accurate detection based on brain…

The analysis of lifelogs can yield valuable insights into an individual's daily life, particularly with regard to their health and well-being. The accurate assessment of quality of life is necessitated by the use of diverse sensors and…

Obesity is a serious public health concern world-wide, which increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers across the health ecosystem are collecting diverse…

In the realm of construction safety, the detection of personal protective equipment, such as helmets, plays a critical role in preventing workplace injuries. This paper details the development and evaluation of convolutional neural networks…

Physical activity and sleep play a major role in the prevention and management of many chronic conditions. It is not a trivial task to understand their impact on chronic conditions. Currently, data from electronic health records (EHRs),…

Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions. While previous studies have captured physiological signals using wearable devices in laboratory settings,…

Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment…

This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate…

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most…

Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high…

The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features which require prior knowledge of sleep…