Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

Federico Cruciani, I Cleland, CD Nugent, P McCullagh, Kare Synnes, Josef Hallberg

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
113 Downloads (Pure)


Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such
datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate
the robustness of common supervised classification approaches under instances of noisy data.
We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated
labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also
demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the
classification approach (Nearest Centroid and Multi-Class Support Vector Machine).
Original languageEnglish
Article number2203
Issue number7
Publication statusPublished (in print/issue) - 9 Jul 2018


  • human activity recognition
  • supervised machine learning
  • label noise
  • automatic annotation
  • inertial sensors
  • smartphone


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