AIMC Topic: Exercise
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Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms.
Sensors (Basel, Switzerland)
Jun 15, 2024
Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, spo...
A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.
Research quarterly for exercise and sport
Jun 14, 2024
With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. A total of 12,712 participants, exc...
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.
Scientific reports
May 31, 2024
Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wear...
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...
The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.
Nutrition bulletin
May 21, 2024
A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hypertension and some cancers. Advances in information computer technology offer the opportunity to pr...
Prediction of adolescent weight status by machine learning: a population-based study.
BMC public health
May 20, 2024
BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have be...
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...
Workout Classification Using a Convolutional Neural Network in Ensemble Learning.
Sensors (Basel, Switzerland)
May 15, 2024
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizin...
A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.
Sensors (Basel, Switzerland)
May 11, 2024
Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and ener...
Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach.
BMC public health
May 7, 2024
BACKGROUND: Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of ...
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