Development of Novel Techniques to Classify Physical Activity Mode Using Accelerometers

Pober, David M.; Staudenmayer, John; Raphael, Christopher; Freedson, Patty S.

doi:10.1249/01.mss.0000227542.43669.45

Cited by 190 publications

(171 citation statements)

References 32 publications

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“…Advances have, however, been made in accelerometer data processing with the development of more sophisticated approaches to data modelling analysis (Bonomi et al 2009, Pober et al 2006, Staudenmayer et al 2009, Zhang et al 2003. This area warrants further investigation in studies of pre-school children to determine if this will offer an accurate means of classifying physical activity behaviour.…”

Section: Discussionmentioning

confidence: 99%

Validity of wrist worn accelerometers and comparability between hip and wrist placement sites in estimating physical activity behaviour in preschool children

Hislop

Palmer²,

Anand

et al. 2016

Physiol. Meas.

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Wrist-worn accelerometers can increase compliance with wearing accelerometers, however, several large scale studies continue to use hip-worn accelerometers and it is unclear how comparable data is from the two sites. The study aims were: to investigate agreement between wrist-and hip-worn accelerometers and to determine the validity of Johansson et al cut-points for wrist worn accelerometers in preschool children.A sample of 32 preschool children (21 boys, 4.2 (0.5) years, BMI 16.6 (1.1)) were videoed wearing GT3X+ accelerometers on their wrist and hip while they engaged in 1 hour of free-play in their nursery.Children's activity were coded using, the Children's Activity Rating Scale (CARS): with CARS, level 1 'sedentary' and levels 2 to 5 were classified as time spent in TPA. Accelerometry data were processed using Johansson et al cut-points for the wrist data and Evenson et al cut-points for the hip data, into time spent in different intensities of PA. The mean counts per minute (cpm) from the hip and wrist were compared.There was a strong correlation between the hip and wrist cpm (r=0.81, p<0.01) and total count data (r=0.83 p<0.01), however there was a large systematic bias with wide limits of agreement. Good agreement (mean difference (LOA) 1.1 (-9.9, 12.1) was found between the CARS estimate of TPA (29.

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Section: Discussionmentioning

confidence: 99%

Validity of wrist worn accelerometers and comparability between hip and wrist placement sites in estimating physical activity behaviour in preschool children

Hislop

Palmer²,

Anand

et al. 2016

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…1 Pober et al (2006) did compare the use of cut points to more advanced methods. However, they used counts, and not raw data, for all analyses.…”

Section: Discussionmentioning

confidence: 99%

“…In principle, different methods are suited for classification (for an extensive overview of classification techniques, see Preece et al, 2009). The methods used include k-nearest neighbor (e.g., Bao & Intille, 2004;Zhang, Rowlands, Murray, & Hurst, 2012), hidden Markov models (e.g., Pober, Staudenmayer, Raphael, & Freedson, 2006), and artificial neural networks (e.g., Hagenbuchner, Cliff, Trost, van Tuc, & Peoples, 2015;Staudenmayer, Pober, Crouter, Bassett, & Freedson, 2009). The Bstate-of-the-art^method that has proven effective for such tasks is the use of support vector machines (SVMs; e.g., He & Jin, 2009).…”

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confidence: 99%

Individual classification of elementary school children’s physical activity: A time-efficient, group-based approach to reference measurements

2016

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The objective measurement of physical activity using accelerometers is becoming increasingly popular. There is little consensus, however, about how to analyze acceleration data. One promising approach is the use of reference measurements in which the subjects conduct specific activities. This makes it possible to identify data patterns that indicate these activities for each subject. The drawback of this approach is its rather high cost, in terms of both time and money. We propose a new approach in which a group of children conduct the reference measurements at the same time. We trained support vector machine models on the accelerometer data of 70 children (ages 8-11 years) to predict their activities during those reference measurements. We correctly classified activities with an accuracy of 96.9 % when fitting the individual models for each subject, and 87.5 % when fitting general models for all subjects. The obtained accuracies were comparable to results reported in previous reference measurement studies, in which each subject was measured individually. They were higher than the accuracies obtained by the traditional approach, which transfers accelerometer data to counts and classifies those on the basis of predefined cut points. We concluded that our approach can yield a valuable contribution, particularly to studies with larger samples.

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“…Generic activity categories are classified with a relatively small number of accelerometers. Pober et al 53 reported an accuracy of 81% for identifying walking and certain lifestyle activities, like vacuum cleaning and working on a computer, Activity monitoring and health AG Bonomi and KR Westerterp by using an Actigraph accelerometer (Actigraph). We showed that identifying three different postures, that is, lying, sitting and standing, and three types of locomotion movements, that is, walking, running and cycling, was accurately achieved using a single accelerometer positioned at the waist.…”

Section: Activity Recognition Using Multiple or Single-site Accelerommentioning

confidence: 99%

Advances in physical activity monitoring and lifestyle interventions in obesity: a review

Bonomi

Westerterp

2011

Int J Obes

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Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.

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Development of Novel Techniques to Classify Physical Activity Mode Using Accelerometers

Abstract: The novel approach of estimating activity mode, rather than activity level, may allow for more accurate field-based estimates of physical activity using accelerometer data, and this approach warrants more study in a larger and more diverse population of subjects and activities.

Cited by 190 publications

References 32 publications

Validity of wrist worn accelerometers and comparability between hip and wrist placement sites in estimating physical activity behaviour in preschool children

Validity of wrist worn accelerometers and comparability between hip and wrist placement sites in estimating physical activity behaviour in preschool children

Individual classification of elementary school children’s physical activity: A time-efficient, group-based approach to reference measurements

Advances in physical activity monitoring and lifestyle interventions in obesity: a review

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