Background Objective measures using accelerometer-based activity monitors have been
extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure …

• Divergence in summary estimates within and between devices obstructs efforts to pool data
and preclude betweenstudy comparisons, which are necessary to develop coherent public …

Purpose Machine-learning (ML) approaches have been repeatedly coupled with raw
accelerometry to classify physical activity classes, but the features required to optimize their …

Purpose To compare physical activity estimates across different accelerometer wear
locations, wear time protocols, and data processing techniques. Methods A convenience …

Background: The term posthospital syndrome has been used to describe the condition in
which older patients are transiently frail after hospitalization and have a high chance of …

PURPOSE To investigate whether the use of ensemble learning algorithms improve
physical activity recognition accuracy compared to the single classifier algorithms, and to …

This paper proposes the use of posterior-adapted class-based weighted decision fusion to
effectively combine multiple accelerometer data for improving physical activity recognition …

This study had three purposes, all related to evaluating energy expenditure (EE) prediction
accuracy from body-worn accelerometers:(1) compare linear regression to linear mixed …

The monitoring of treadmill walking energy expenditure (EE) plays an important role in
health evaluations and management, particularly in older individuals and those with chronic …

Background: Accurate solutions for the estimation of physical activity and energy
expenditure at scale are needed for a range of medical and health research fields. Machine …