Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: Implications of age

Scott J. Strath*, Rohit J. Kate, Kevin G. Keenan, Whitney A. Welch, Ann M. Swartz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18-39, 40-64, 65 + years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of -0.03 to 0.01 METs, bias percent of -0.8 to 0.3%, and a rMSE range of 0.81-1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed.

Original languageEnglish (US)
Pages (from-to)2335-2351
Number of pages17
JournalPhysiological Measurement
Volume36
Issue number11
DOIs
StatePublished - Oct 9 2015

Keywords

  • ActiGraph
  • activity recognition
  • assessment
  • motion sensor
  • objective monitoring

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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