AIMC Topic: Sedentary Behavior

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A Machine Learning Approach for Prediction of Sedentary Behavior Based on Daily Step Counts.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sedentary behavior is considered as a major public health challenge, linked with many chronic diseases and premature mortality. In this paper, we propose a steps counting -based machine learning approach for the prediction of sedentary behavior. Our ...

Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers.

Medicine and science in sports and exercise
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...

A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults.

Medicine and science in sports and exercise
INTRODUCTION: Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accur...

Towards a Personal Health Record System for the Assesment and Monitoring of Sedentary Behavior in Indoor Locations.

Studies in health technology and informatics
BACKGROUND: Sedentary behavior has been associated to the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Accelerometers and inclinometers have been used to estimate sedentary behavior...