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Actigraphy

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Phase Relationship between DLMO and Sleep Onset and the Risk of Metabolic Disease among Normal Weight and Overweight/Obese Adults.

Journal of biological rhythms
Circadian misalignment is hypothesized to contribute to increased diabetes and obesity among shift workers and individuals with late sleep timing. Accordingly, the goal of our study was to identify-among normal and overweight/obese adults-association...

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors.

BioMed research international
Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and a...

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

Journal of dairy science
The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from Se...

Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Atopic dermatitis is a chronic inflammatory skin condition affecting both children and adults and is associated with pruritus. A method for objectively quantifying nocturnal scratching events could aid in the development of therapies for atopic derma...

Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification.

Computers in biology and medicine
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respecti...

Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpe...

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...

Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

Computers in biology and medicine
BACKGROUND: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wri...

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Journal of sleep research
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here...

Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study.

JMIR mHealth and uHealth
BACKGROUND: Wearable accelerometers have greatly improved measurement of physical activity, and the increasing popularity of smartwatches with inherent acceleration data collection suggest their potential use in the physical activity research domain;...