scholarly journals Employing Machine Learning to Estimate Hallmark Measures of Physical Activities from Wrist-worn Devices Across Age Groups

2021 ◽  
Author(s):  
Mamoun T. Mardini ◽  
Chen Bai ◽  
Amal A. Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Energy Expenditure ◽  
Age Groups ◽  
Physical Activities ◽  
Accelerometer Data ◽  
Activity Type ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Score Range

Wrist-worn fitness trackers and smartwatches are proliferating with an incessant attention towards health tracking. Given the growing popularity of wrist-worn devices across all age groups, a rigorous evaluation for recognizing hallmark measures of physical activities and estimating energy expenditure is needed to compare their accuracy across the lifespan. The goal of the study was to build machine learning models to recognize physical activity type (sedentary, locomotion, and lifestyle) and intensity (low, light, and moderate), identify individual physical activities, and estimate energy expenditure. The primary aim of this study was to build and compare models for different age groups: young [20-50 years], middle (50-70 years], and old (70-89 years]. Participants (n = 253, 62% women, aged 20-89 years old) performed a battery of 33 daily activities in a standardized laboratory setting while wearing a portable metabolic unit to measure energy expenditure that was used to gauge metabolic intensity. Tri-axial accelerometer collected data at 80-100 Hz from the right wrist that was processed for 49 features. Results from random forests algorithm were quite accurate in recognizing physical activity type, the F1-Score range across age groups was: sedentary [0.955 – 0.973], locomotion [0.942 – 0.964], and lifestyle [0.913 – 0.949]. Recognizing physical activity intensity resulted in lower performance, the F1-Score range across age groups was: sedentary [0.919 – 0.947], light [0.813 – 0.828], and moderate [0.846 – 0.875]. The root mean square error range was [0.835 – 1.009] for the estimation of energy expenditure. The F1-Score range for recognizing individual physical activities was [0.263 – 0.784]. Performances were relatively similar and the accelerometer data features were ranked similarly between age groups. In conclusion, data features derived from wrist worn accelerometers lead to high-moderate accuracy estimating physical activity type, intensity and energy expenditure and are robust to potential age-differences.

scholarly journals Reexamination of Accelerometer Calibration with Energy Expenditure as Criterion: VO2net Instead of MET for Age-Equivalent Physical Activity Intensity

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3377 ◽  
Author(s):  
Daniel Arvidsson ◽  
Jonatan Fridolfsson ◽  
Christoph Buck ◽  
Örjan Ekblom ◽  
Elin Ekblom-Bak ◽  
...  
Keyword(s):  
Physical Activity ◽  
Age Groups ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Free Living ◽  
Activity Intensity ◽  
Age Related ◽  
Younger Age ◽  
Metabolic Equivalent Of Task ◽  
Adolescents And Adults

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO2net (VO2gross − VO2stand; mL⋅min−1⋅kg−1) as criterion compared to MET (VO2gross/VO2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speedeq) was used as indicator of the absolute speed (Speedabs) performed with the same effort in individuals of different body size/age. The results showed that VO2net was higher in younger age-groups for Speedabs, but was similar in the three age-groups for Speedeq. MET was lower in younger age-groups for both Speedabs and Speedeq. The same VO2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research.

Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification

2020 ◽  
Vol 76 ◽  
pp. 104-109 ◽  
Author(s):  
Florêncio Diniz-Sousa ◽  
Lucas Veras ◽  
José Carlos Ribeiro ◽  
Giorjines Boppre ◽  
Vítor Devezas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Class Ii ◽  
Physical Activity Intensity ◽  
Activity Intensity

Predicting Substance Use from Physical Activity Intensity in Adolescents

2004 ◽  
Vol 16 (3) ◽  
pp. 277-289 ◽  
Author(s):  
Kerri McCaul ◽  
Joseph Baker ◽  
John K. Yardley
Keyword(s):  
Physical Activity ◽  
Substance Use ◽  
Alcohol Use ◽  
Positive Relationship ◽  
Activity Type ◽  
Physical Activity Intensity ◽  
Individual Activity ◽  
Low Intensity ◽  
Activity Intensity ◽  
Negative Relationships

Adolescence is characterized as a period of change and adaptation typically marked by a decline in physical activity participation and accompanied by an increase in substance use. The purpose of this study was to examine the relationships among the type (team and individual activity) and intensity (high, medium, and low intensity) of physical activity and substance use (tobacco, marijuana, and alcohol use, and binge drinking) in a sample of 738 adolescents. Results indicated differing relationships among study variables depending on the type and intensity of physical activity and the type of substance used For instance, a positive relationship was found for physical activity intensity and alcohol use, but negative relationships were found for physical activity and tobacco and marijuana use. Collectively, the results reveal that the relationships between physical activity type and intensity and substance use are more complex than previously believed.

scholarly journals Are Machine Learning Models Used to Represent Accelerometry Data Robust to Age Differences?

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 784-785
Author(s):  
Mamoun Mardini ◽  
Chen Bai ◽  
Amal Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Age Differences ◽  
Age Groups ◽  
Model Performance ◽  
Learning Models ◽  
Metabolic Intensity ◽  
The Right ◽  
Score Range ◽  
Machine Learning Models

Abstract Regular and sufficient amounts of physical activity (PA) are significant in increasing health benefits and mitigating health risks. Given the growing popularity of wrist-worn devices across all age groups, a rigorous evaluation for recognizing hallmark measures of physical activities and estimating energy expenditure is needed to compare their accuracy across the lifespan. The goal of the study was to build machine learning models to recognizing the hallmark measures of PA and estimating energy expenditure (EE), and to test the hypothesis that model performance varies across age-group: young [20-50 years], middle (50-70 years], and old (70-89 years]. Participants (n = 253, 62% women, aged 20-89 years old) performed a battery of 33 daily activities in a standardized laboratory setting while wearing a portable metabolic unit to measure EE that was used to gauge metabolic intensity. Participants also wore a Tri-axial accelerometer on the right wrist. Results from random forests algorithm were quite accurate at recognizing PA type; the F1-Score range across age groups was: sedentary [0.955 – 0.973], locomotion [0.942 – 0.964], and lifestyle [0.913 – 0.949]. Recognizing PA intensity resulted in lower performance; the F1-Score range across age groups was: sedentary [0.919 – 0.947], light [0.813 – 0.828], and moderate [0.846–0.875]. The root mean square error range was [0.835–1.009] for the estimation of EE. The F1-Score range for recognizing individual PAs was [0.263–0.784]. In conclusion, machine learning models used to represent accelerometry data are robust to age differences and a generalizable approach might be sufficient to utilize in accelerometer-based wearables.

When American Adults Do Move, How Do They Do So? Trends in Physical Activity Intensity, Type, and Modality: 1988–2017

2021 ◽  
pp. 1-18
Author(s):  
Matthew A. Ladwig ◽  
Christopher N. Sciamanna ◽  
Brandon J. Auer ◽  
Tamara K. Oser ◽  
Jonathan G. Stine ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Activities ◽  
Behavioral Risk ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Using Data ◽  
Sport And Exercise ◽  
Over Time

Background: Few Americans accumulate enough physical activity (PA) to realize its benefits. Understanding how and why individuals use their discretionary time for different forms of PA could help identify and rectify issues that drive individuals away from certain physical activities, and leverage successful strategies to increase participation in others. Methods: The authors analyzed approximately 30 years of changes in PA behavior by intensity, type, and mode, using data from the Behavioral Risk Factor Surveillance System. Results: Since 1988, the proportions of adults most frequently engaging in exercise, sport, or lifestyle physical activity have changed noticeably. The most apparent changes from 1988 to 2017 were the proportions most frequently engaging in Exercise and Sport. In addition, the proportion of time reportedly spent in vigorous-intensity PA decreased over time, particularly among male respondents. Moreover, the proportion of Americans reporting an “Other” PA mode increased substantially, suggesting a growing need for a greater variety of easily accessible options for adult PA. Conclusions: Over time, a smaller proportion of American adults reported participating in sport and exercise modalities and reported engaging more frequently in low-intensity physical activities.

scholarly journals Ability of RT3 Accelerometer Cut Points to Detect Physical Activity Intensity in Ambulatory Children With Cerebral Palsy

2014 ◽  
Vol 31 (4) ◽  
pp. 310-324 ◽  
Author(s):  
Jennifer Ryan ◽  
Michael Walsh ◽  
John Gormley
Keyword(s):  
Physical Activity ◽  
Cerebral Palsy ◽  
Vigorous Physical Activity ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Cut Points ◽  
Cut Point ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Children With Cerebral Palsy

This study investigated the ability of published cut points for the RT3 accelerometer to differentiate between levels of physical activity intensity in children with cerebral palsy (CP). Oxygen consumption (metabolic equivalents; METs) and RT3 data (counts/min) were measured during rest and 5 walking trials. METs and corresponding counts/min were classified as sedentary, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) according to MET thresholds. Counts were also classified according to published cut points. A published cut point exhibited an excellent ability to classify sedentary activity (sensitivity = 89.5%, specificity = 100.0%). Classification accuracy decreased when published cut points were used to classify LPA (sensitivity = 88.9%, specificity = 79.6%) and MVPA (sensitivity = 70%, specificity = 95–97%). Derivation of a new cut point improved classification of both LPA and MVPA. Applying published cut points to RT3 accelerometer data collected in children with CP may result in misclassification of LPA and MVPA.

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