Aims: Develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) level in ambulatory youth with CP; and 2) compare the classification accuracy of the decision tree models to that achieved by previously published cut-points for youth with ...
Introduction: Wrist-worn accelerometers are convenient to wear and are associated with greater compliance. However, validated algorithms for predicting activity type and/or energy expenditure from wrist-worn accelerometer data are lacking. Purpose: To compare the activity recognition rates of an activity classifier trained on raw tri-axial acceleration signal (30 Hz) collected on ...
Objectives: Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design: Participants completed 12 standardised activity trials (TV, reading, tablet ...
Purpose: The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers. Methods: Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg·m (2.5 kg·m)) performed 15 ...
Background: Physical activity patterns captured by accelerometers have been used to classify activity type with machine learning (ML) algorithms. ML may also be applied to accelerometer data for predicting cardiovascular (CV) health risk directly. Decision trees are efficient constructive search algorithms that develop rules for categorizing the data based ...
Purpose: To compare activity type recognition rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in free-living older adults. Methods: Thirty-seven older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities (total of 35 min) in the lab while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, ...
Purpose To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3...
Introduction In classifying actigraphy signals, features are usually extracted from raw data and then fed to some sort of a classifier that determines the type and level of activity. A huge variety of features have been tried by many researchers in this field. Some features are statistical in nature, some ...
Introduction Tri-axial accelerometers record the acceleration of people’s daily activity on three orthogonal directions. One fundamental question is how to decipher and interpret the acceleration signals into meaningful information such as types of human movement. Purpose We provide statistical methods for predicting activity type and answer the following questions: 1) ...
Purpose Wrist accelerometers are being used in population level surveillance (i.e. NHANES) of physical activity (PA) but more research is needed to evaluate the validity of a wrist-worn device for predicting PA. In this study we compare accelerometers worn on the wrist and each hip for predicting PA type ...