Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions
Journal
PloS one
Date Issued
2017-09-07
Author(s)
Risteska Stojkoska, Biljana
Standl, Marie
Schulz, Holger
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.
Methods
The study used diarized jogging periods and the corresponding accelerometer data from
thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus
study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine
learning algorithms were used for classification: Logistic regression, Support Vector
Machines, Random Forest and Extremely Randomized Trees. Classification was performed
using only data from the hip accelerometer, using only data from ankle accelerometer and
using data from both accelerometers.
Results
The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation
strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of
correctly identified jogging periods related to the total time including the missed ones, was
up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious
benefit of using two accelerometers, rather almost the same performance could be achieved
from either accelerometer position.
Conclusions
Machine learning techniques can be used for automatic activity recognition, as they provide
very accurate activity recognition, significantly more accurate than when keeping a diary.
Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both
locations.
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.
Methods
The study used diarized jogging periods and the corresponding accelerometer data from
thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus
study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine
learning algorithms were used for classification: Logistic regression, Support Vector
Machines, Random Forest and Extremely Randomized Trees. Classification was performed
using only data from the hip accelerometer, using only data from ankle accelerometer and
using data from both accelerometers.
Results
The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation
strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of
correctly identified jogging periods related to the total time including the missed ones, was
up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious
benefit of using two accelerometers, rather almost the same performance could be achieved
from either accelerometer position.
Conclusions
Machine learning techniques can be used for automatic activity recognition, as they provide
very accurate activity recognition, significantly more accurate than when keeping a diary.
Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both
locations.
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