Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population

HIGHLIGHTS

• who: Christopher B. Thornton and colleagues from the Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom have published the research work: Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population, in the Journal: (JOURNAL)
• what: The authors show that an unsupervised machine learning technique (the hidden semi-Markov model) can be used to estimate categories of activity intensity in accelerometry data recorded from a diverse population of children age 9-36 months. The authors demonstrate that this method is a more appropriate approach . . .
  

   

  

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