31
IRUS Total
Downloads

Unsupervised domain adaptation for position-independent IMU based gait analysis

File Description SizeFormat 
Sensors_Conf_20.pdfAccepted version3.79 MBAdobe PDFView/Open
Title: Unsupervised domain adaptation for position-independent IMU based gait analysis
Authors: Mu, F
Gu, X
Guo, Y
Lo, B
Item Type: Conference Paper
Abstract: Inertial measurement units (IMUs) together with advanced machine learning algorithms have enabled pervasive gait analysis. However, the worn positions of IMUs can be varied due to movements, and they are difficult to standardize across different trials, causing signal variations. Such variation contributes to a bias in the underlying distribution of training and testing data, and hinder the generalization ability of a computational gait analysis model. In this paper, we propose a position-independent IMU based gait analysis framework based on unsupervised domain adaptation. It is based on transferring knowledge from the trained data positions to a novel position without labels. Our framework was validated on gait event detection and pathological gait pattern recognition tasks based on different computational models and achieved consistently high performance on both tasks.
Issue Date: 25-Oct-2020
Date of Acceptance: 25-Aug-2020
URI: http://hdl.handle.net/10044/1/88022
DOI: 10.1109/sensors47125.2020.9278863
Publisher: IEEE
Start Page: 1
End Page: 4
Journal / Book Title: 2020 IEEE SENSORS
Copyright Statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
British Council (UK)
British Council (UK)
Funder's Grant Number: EP/K503733/1
330760239
2017-RLWK9-11046
Conference Name: 2020 IEEE SENSORS
Publication Status: Published
Start Date: 2020-10-25
Finish Date: 2020-10-28
Conference Place: Rotterdam, Netherlands
Online Publication Date: 2020-12-09
Appears in Collections:Department of Surgery and Cancer
Computing
Faculty of Medicine
Institute of Global Health Innovation