A Public Domain Dataset for Human Activity Recognition in Free-living Conditions

Federico Cruciani, Chen Sun, Shuai Zhang, CD Nugent, Chunping Li, Shaoxu Song, Cheng Cheng, I Cleland, P McCullagh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In Human Activity Recognition (HAR), supervised
Machine Learning methods are predominantly used, making
availability of datasets a major issue for research in the field.
In particular, the majority of available datasets are collected
under controlled conditions. Consequently, models trained under
similar circumstances, generally exhibit a significant decrease in
recognition accuracy when they are moved to final deployment
in the wild, within unconstrained settings. This paper presents a
new dataset for HAR, collected in free-living and unconstrained
conditions. 10 subjects were recruited for the purpose of data
collection. Data was recorded over a 6 week period using
a smartphone app, and a wristband activity monitor. During
the first and last week of observation, participants also wore
an ActivPAL™ activity logger. The data collected have been
partially self-labeled by participants, by means of the mobile app
provided for data collection. The dataset collected can be used to
evaluate HAR algorithm and models in real-world unconstrained
settings. Together with the description of the dataset, this work
presents some preliminary results, obtained cross-validating a
model trained on the publicly available Extrasensory dataset, and
testing its performance on our newly collected dataset. Results
obtained highlighted high cross-subject variability when testing
on new subjects, with a balanced accuracy varying between
53.33% and 90.01%, with an average balanced accuracy of
71.73%.
Original languageEnglish
Title of host publicationThe 2nd Workshop on advanced Technologies for Smarter Assisted Living solutions: Towards an open Smart Home infrastructure (SmarterAAL'19)
Pages166-171
Number of pages6
Publication statusAccepted/In press - 21 May 2019

    Fingerprint

Keywords

  • Balanced Batch Learning
  • Convolutional Neural Networks
  • Dataset
  • Free-living
  • Human Activity Recognition
  • Machine Learning

Cite this

Cruciani, F., Sun, C., Zhang, S., Nugent, CD., Li, C., Song, S., ... McCullagh, P. (Accepted/In press). A Public Domain Dataset for Human Activity Recognition in Free-living Conditions. In The 2nd Workshop on advanced Technologies for Smarter Assisted Living solutions: Towards an open Smart Home infrastructure (SmarterAAL'19) (pp. 166-171)