research-article

GaitAssist: a daily-life support and training system for parkinson's disease patients with freezing of gait

Authors:

Sinziana Mazilu,

Michael Hardegger,

Gerhard Tröster,

Jeffrey M. HausdorffAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 2531 - 2540

https://doi.org/10.1145/2556288.2557278

Published: 26 April 2014 Publication History

Abstract

Patients with Parkinson's disease often experience freezing of gait, which bears a high risk of falling, a prevalent cause for morbidity and mortality. In this work we present GaitAssist, a wearable system for freezing of gait support in daily life. The system provides real-time auditory cueing after the onset of freezing episodes. Furthermore, GaitAssist implements training exercises to learn how to handle freezing situations. GaitAssist is the result of a design process where we considered the input of engineers, clinicians and 18 Parkinson's disease patients, in order to find an optimal trade-off between system wearability and performance. We tested the final system in a user study with 5 additional patients. They reported a reduction in the freezing of gait duration as a result of the auditory stimulation provided, and that they feel the system enhanced their confidence during walking.

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Cited By

Koltermann KClapham JBlackwell GJung WBurnet EGao YShao HCloud LPretzer-Aboff IZhou G(2024)Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE60773.2024.00016(61-72)Online publication date: 19-Jun-2024
https://doi.org/10.1109/CHASE60773.2024.00016
Wang BHu XGe RXu CZhang JGao ZZhao SPolat K(2024)Prediction of Freezing of Gait in Parkinson’s disease based on multi-channel time-series neural networkArtificial Intelligence in Medicine10.1016/j.artmed.2024.102932154(102932)Online publication date: Aug-2024
https://doi.org/10.1016/j.artmed.2024.102932
Zhang WSun HHuang DZhang ZLi JWu CSun YGong MWang ZSun CCui GGuo YChan P(2023)Detection and prediction of freezing of gait with wearable sensors in Parkinson’s diseaseNeurological Sciences10.1007/s10072-023-07017-y45:2(431-453)Online publication date: 16-Oct-2023
https://doi.org/10.1007/s10072-023-07017-y
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Index Terms

GaitAssist: a daily-life support and training system for parkinson's disease patients with freezing of gait
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 April 2014

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CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

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CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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https://doi.org/10.1109/CHASE60773.2024.00016
Wang BHu XGe RXu CZhang JGao ZZhao SPolat K(2024)Prediction of Freezing of Gait in Parkinson’s disease based on multi-channel time-series neural networkArtificial Intelligence in Medicine10.1016/j.artmed.2024.102932154(102932)Online publication date: Aug-2024
https://doi.org/10.1016/j.artmed.2024.102932
Zhang WSun HHuang DZhang ZLi JWu CSun YGong MWang ZSun CCui GGuo YChan P(2023)Detection and prediction of freezing of gait with wearable sensors in Parkinson’s diseaseNeurological Sciences10.1007/s10072-023-07017-y45:2(431-453)Online publication date: 16-Oct-2023
https://doi.org/10.1007/s10072-023-07017-y
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Meshram ARai B(2021)User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest ClassificationResearch Anthology on Diagnosing and Treating Neurocognitive Disorders10.4018/978-1-7998-3441-0.ch023(456-472)Online publication date: 2021
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Diep CO’Day JKehnemouyi YBurnett GBronte-Stewart H(2021)Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place TaskSensors10.3390/s2108266121:8(2661)Online publication date: 10-Apr-2021
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