AIMC Topic: Accelerometry

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Automated remote sleep monitoring needs uncertainty quantification.

Journal of sleep research
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...

Deep Learning System for User Identification Using Sensors on Doorknobs.

Sensors (Basel, Switzerland)
Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring ...

Identification of footstrike pattern using accelerometry and machine learning.

Journal of biomechanics
Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be d...

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Sensors (Basel, Switzerland)
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented m...

Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Epilepsia
Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neu...

Automated Detection of In-Home Activities with Ultra-Wideband Sensors.

Sensors (Basel, Switzerland)
As Canada's population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitorin...

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

IEEE transactions on bio-medical engineering
Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using w...

Evaluating machine learning algorithms to predict lameness in dairy cattle.

PloS one
Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lamen...

GMAC-A Simple Measure to Quantify Upper Limb Use From Wrist-Worn Accelerometers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use - thresholded activity counts (TAC) and the gross movement (GM) score suffer fro...

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.

Sensors (Basel, Switzerland)
The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably ...