Monitoring Motor Activity Data for Detecting Patients' Depression Using Data Augmentation and Privacy-Preserving Distributed Learning

Amin Aminifar1, Fazle Rabbi1,2, Violet Ka I Pun1,3, Yngve Lamo1
1 Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Norway
2 Department of Information Science and Media Studies, University of Bergen, Norway
3 Department of Informatics, University of Oslo, Norway
43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
DOI: https://doi.org/10.1109/EMBC46164.2021.9630592

PDF Download PDF BibTeX Citation Google Scholar Google Scholar

Graphical abstract

Analysis of sensor data generated by wearable devices
Analysis of sensor data generated by wearable devices

Abstract

Wearable devices are currently being considered to collect personalized physiological information, which is lately being used to provide healthcare services to individuals. One application is detecting depression by utilization of motor activity signals collected by the ActiGraph wearable wristbands. However, to develop an accurate classification model, we require to use a sufficient volume of data from several subjects, taking the sensitivity of such data into account. Therefore, in this paper, we present an approach to extract classification models for predicting depression based on a new augmentation technique for motor activity data in a privacy-preserving fashion. We evaluate our approach against the state-of-the-art techniques and demonstrate its performance based on the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) Project.

BibTeX

@inproceedings{aminifar2021monitoring,
  title={Monitoring motor activity data for detecting patients’ depression using data augmentation and privacy-preserving distributed learning},
  author={Aminifar, Amin and Rabbi, Fazle and Pun, Violet Ka I and Lamo, Yngve},
  booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  pages={2163--2169},
  year={2021},
  organization={IEEE}
}