A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity
| Autor: | Kelly A. Mackintosh, Lynne M. Boddy, John Hearty, Paul Fergus, Nicola D. Ridgers, Stuart J. Fairclough, Naeem Radi, Abir Hussain, Gareth Stratton |
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| Přispěvatelé: | Fergus, P, Hussain, A, Hearty, J, Fairclough, S, Boddy, L, Mackintosh, KA, Stratton, G, Ridgers, ND, Radi, Naeem |
| Rok vydání: | 2015 |
| Předmět: |
obesity
medicine.medical_specialty Computer science Physical activity physical activity Overweight sensors Machine learning computer.software_genre Childhood obesity medicine Feature (machine learning) overweight Set (psychology) Wearable technology Measure (data warehouse) business.industry Public health neural networks medicine.disease Obesity machine learning Artificial intelligence medicine.symptom business computer |
| Zdroj: | Intelligent Computing Theories and Methodologies ISBN: 9783319221854 ICIC (2) |
| Popis: | 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. usc |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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