Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey
Loading...
Links to Files
Author/Creator
Author/Creator ORCID
Date
2023-04-07
Type of Work
Department
Program
Citation of Original Publication
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Subjects
Abstract
Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the
widespread adoption of these WHAR models is impeded by their degraded performance in the
presence of data distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and environmental
diversities. Various traditional machine learning algorithms and transfer learning techniques
have been proposed in the literature to address the underpinning challenges of handling such
data heterogeneities. Domain adaptation is one such transfer learning techniques that has
gained significant popularity in recent literature. In this paper, we survey the recent progress of
domain adaptation techniques in the Inertial Measurement Unit (IMU)-based human activity
recognition area, discuss potential future directions.