Identification of Children's Activity Type with Accelerometer-Based Neural Networks

Vries, S.I. de; Engels, Marjolein; Garre, Francisca Galindo

doi:10.1249/mss.0b013e318219d939

Cited by 37 publications

(46 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study carried out by De Vries et al, a series of ANNs were developed to predict PA in children across a range of activity types. However, the results reported were significantly lower than those reported in (Staudenmayer et al, 2009) with classification accuracies between 57.2% and 76.8% (De Vries et al, 2011;De Vries, Garre, Engbers, H., & Van Buuren, 2001). …”

Section: Introductioncontrasting

confidence: 63%

“…Artificial neural networks (ANN) have been used to classify physical activity (De Vries, Engels, & Garre, 2011). In one study, Staudenmayer et al developed an ANN, to classify activity type in adults, using time windows, with 88% overall accuracy and a consistently low Root Mean Squared Error (rMSE) measure (Staudenmayer, Prober, Crouter, Bassett, & Freedson, 2009).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

Fergus

Hussain

Hearty

et al. 2015

Intelligent Computing Theories and Methodologies

View full text Add to dashboard Cite

Abstract. Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96% with 95% for sensitivity, 99% for specificity and a kappa value of 94% when three and four feature combinations were used.

show abstract

Section: Introductioncontrasting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

Fergus

Hussain

Hearty

et al. 2015

Intelligent Computing Theories and Methodologies

View full text Add to dashboard Cite

show abstract

“…Studies have shown their usefulness, but only when specific activities are used [31]. Consequently, the type of activities used may also cause variance in the accelerometer readings obtained.…”

Section: Accelerometry and Cut Pointsmentioning

confidence: 99%

“…Artificial neural networks (ANN) have been used to classify physical activity in several studies, with good results [31]. In one such study, Staudenmayer et al…”

Section: Physical Activity Classificationmentioning

confidence: 99%

A machine learning approach to measure and monitor physical activity in children

Fergus

Hussain

Hearty³

et al. 2017

Neurocomputing

View full text Add to dashboard Cite

ABSTRACT-Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments.The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases.

show abstract

“…Mean accuracy for activity type ranged from 81.3% to 88.4%. De Vries et al trained an ANN to predict 9-12 year old children's PA type from accelerometers worn on the hip and ankle 7 . The overall classification accuracy across the seven activity types evaluated ranged from 57.2% (GT1M/ankle placement) to 76.8% (GT3X/hip placement).…”

Section: Introductionmentioning

confidence: 99%

Prediction of activity type in preschool children using machine learning techniques

Hagenbuchner

Cliff

Trost

et al. 2015

Journal of Science and Medicine in Sport

View full text Add to dashboard Cite

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 game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3-6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderateto-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children. KeywordsPhysical activity, pattern recognition, accelerometry, neural networks, exercise, validity Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication DetailsHagenbuchner, M., Cliff, D. P., Trost, S. G., Van Tuc, N. & Peoples, G. E. (2015). Prediction of activity type in preschool children using machine learning techniques. Journal of Science and Medicine in Sport, 18 (4) AbstractObjectives: 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 game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits.Methods: Eleven children aged 3-6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial...

show abstract

Identification of Children's Activity Type with Accelerometer-Based Neural Networks

Abstract: Applying ANN models to processing accelerometer data from children is promising for classifying common physical activities. The highest percentage of correctly classified activities was achieved when using triaxial accelerometer data from the hip.

Cited by 37 publications

References 18 publications

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

A machine learning approach to measure and monitor physical activity in children

Prediction of activity type in preschool children using machine learning techniques

Contact Info

Product

Resources

About