AIMC Topic: Fitness Trackers
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Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning.
JAMA network open
Dec 7, 2018
IMPORTANCE: Despite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data s...
Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking.
Journal of biomechanics
Jan 12, 2018
The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen old...
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
Aug 4, 2017
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...
Male
Middle Aged
Case-Control Studies
Time Factors
Health Status
Predictive Value of Tests
Equipment Design
Signal Processing, Computer-Assisted
Reproducibility of Results
Aged
Adult
Humans
Biomechanical Phenomena
Upper Extremity
Machine Learning
Stroke
Video Recording
Female
Fitness Trackers
Actigraphy
Feasibility Studies
Movement
Activities of Daily Living
Acceleration
Modeling long-term human activeness using recurrent neural networks for biometric data.
BMC medical informatics and decision making
May 18, 2017
BACKGROUND: With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three ty...
MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2024
OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the t...
Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.
Studies in health technology and informatics
Jun 29, 2023
Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes...
Combining digital data and artificial intelligence for cardiovascular health.
Cardiovascular research
Jul 27, 2021
Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers.
Medicine and science in sports and exercise
May 1, 2020
UNLABELLED: Machine learning classification models for accelerometer data are potentially more accurate methods to measure physical activity in young children than traditional cut point methods. However, existing algorithms have been trained on labor...
Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology.
Arthritis care & research
Oct 1, 2019
OBJECTIVE: Flares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient-reported flares and activity-tracker-provided steps p...
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