AIMC Topic: Accelerometry
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Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.
Research quarterly for exercise and sport
Jul 3, 2024
To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice ses...
Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.
Sensors (Basel, Switzerland)
Jul 1, 2024
This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabil...
AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.
Computer methods and programs in biomedicine
Jun 7, 2024
BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologis...
CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data.
IEEE journal of biomedical and health informatics
Jun 6, 2024
Alzheimer's disease (AD) is a neurodegenerative disorder that can cause a significant impairment in physical and cognitive functions. Gait disturbances are also reported as a symptom of AD. Previous works have used Convolutional Neural Networks (CNNs...
Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones.
Sensors (Basel, Switzerland)
May 24, 2024
Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrate...
Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.
Journal of science and medicine in sport
May 22, 2024
OBJECTIVES: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based i...
Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.
Sensors (Basel, Switzerland)
May 16, 2024
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation...
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
Medicine and science in sports and exercise
May 15, 2024
PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices agains...
Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.
Sensors (Basel, Switzerland)
May 9, 2024
Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the po...
Accurate fall risk classification in elderly using one gait cycle data and machine learning.
Clinical biomechanics (Bristol, Avon)
May 8, 2024
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...
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