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| Title | A Public Domain Dataset For Human Activity Recognition In Free-Living Conditions |
|---|---|
| ID_Doc | 3893 |
| Authors | Cruciani F.; Sun C.; Zhang S.; Nugent C.; Li C.; Song S.; Cheng C.; Cleland I.; McCullagh P. |
| Year | 2019 |
| Published | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 |
| DOI | http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00071 |
| 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%. © 2019 IEEE. |
| Author Keywords | Balanced Batch Learning; Convolutional Neural Networks; Dataset; Free-living; Human Activity Recognition; Machine Learning |
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