AIMC Topic: Accelerometry

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Deep neural network valve detection for accelerometer based cardiac monitoring.

Scientific reports
Miniaturized accelerometers incorporated in pacing leads attached directly onto the heart provide a means for continuous monitoring of cardiac function. Several functional accelerometer indices first require detection of valve events. We previously d...

Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.

Translational psychiatry
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensors which can continuously and unobtrusively measure behaviors (e.g., activity levels, location, and p...

Processing UK Biobank High Resolution Accelerometry Data for Unsupervised Identification of Activity Profiles and Their Differences in Clinically Relevant Outcome Parameters - The ATLAS Index Revisited.

Studies in health technology and informatics
Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Building on previous work to derive easy-to-interpret activity parameters - the Activity Types from Long-te...

Long Short-Term Memory Network for Accelerometer-Based Hypertension Classification.

Studies in health technology and informatics
This study investigates the application of a Long Short-Term Memory (LSTM) architecture for classifying hypertension using accelerometer data, specifically focusing on physical activity and sleep from the publicly available NHANES 2011-2012 dataset. ...

Hidden Markov model-based similarity measure (HMM-SM) for gait quality assessment of lower-limb prosthetic users using inertial sensor signals.

Journal of neuroengineering and rehabilitation
BACKGROUND: Gait quality indices, such as the Gillette Gait Index or Gait Profile Score (GPS), can provide clinicians with objective, straightforward measures to quantify gait pathology and monitor changes over time. However, these methods often requ...

Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.

Sleep
STUDY OBJECTIVES: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilize...

Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals.

Sensors (Basel, Switzerland)
Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a "snapshot in time" of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for co...

Speech Detection via Respiratory Inductance Plethysmography, Thoracic Impedance, Accelerometers, and Gyroscopes: A Machine Learning-Informed Comparative Study.

Psychophysiology
Speech production interferes with the measurement of changes in cardiac vagal activity during acute stress by attenuating the expected drop in heart rate variability. Speech also induces cardiac sympathetic changes similar to those induced by psychol...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Depression and anxiety
Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and the subjective and objective parameters of sleep in patients with short-term insomnia and construct ma...

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairme...