Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.
Journal:
PloS one
PMID:
28880923
Abstract
BACKGROUND: Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.