Modeling of Ordinal Longitudinal Accelerometer Data

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Contributed Abstract 

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Drew M Lazar (1)

(1) Ball State University, N/A

Drew M Lazar  
Ball State University

Drew M Lazar  
Ball State University

Improvements in accelerometer technology has led to new types of data on which
more powerful predictive models can be built to assess physical activity. This
paper implements an ordinal random forest model with recursive forecasting to
take into account the ordinal longitudinal nature of responses. The data comes
from 28 adults performing activities of daily living in two visits, while wearing
accelerometers on the ankle, hip, right and left wrist. The first visit provided
training data and the second testing data so that an independent sample, cross-validation
approach could be used. For this data, prior responses are not available
at the testing stage or in practice. However, recursive forecasts can be made
with prior predictions in place of lagging responses on models which were built
to use lagging responses as explanatory variables. Models are fit to account
for multiple time series, with different time series for each participant in the
study. We found that ordinal random forest, when the time series is taken into
account, produces better accuracy rates and better linearly weighted kappa values
than both ordinary ordinal forest and random forest. On the testing

Longitudinal Data|Multiple Time Series|Accelerometers|Ordinal Models | |

Section on Statistical Computing

Machine Learning Algorithms