research-article

Quantifying community mobility after stroke using mobile phone technology

Authors:

Megan K. O'Brien,

Chaithanya K. Mummidisetty,

Xiao Bo,

Christian Poellabauer,

Arun JayaramanAuthors Info & Claims

UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Pages 161 - 164

https://doi.org/10.1145/3123024.3123085

Published: 11 September 2017 Publication History

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Abstract

Stroke is a common neurological disorder that can drastically impact mobility - that is, the ability to move in and through one's surroundings. Although rehabilitation programs focus on restoring mobility after stroke, there is no present method for reliably and fully quantifying mobility outside of a clinical setting. Smartphones are ubiquitous wearable sensing systems that can measure community mobility using movement and location tracking. We are currently investigating the ability of smartphone data to characterize post-stroke recovery in a long-term monitoring study (3--6 months). We will evaluate the potential of community mobility features to predict recovery, as determined by traditional clinical assessments. Preliminary results will be available in August 2017. Findings will provide a more objective, complete picture of recovery following stroke as well as the real-world impact of rehabilitation.

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Cited By

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Sprint GCook DFritz R(2020)Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point DetectionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2020.2999607(1-1)Online publication date: 2020
https://doi.org/10.1109/JBHI.2020.2999607
Cook DSchmitter-Edgecombe MJonsson LMorant A(2019)Technology-Enabled Assessment of Functional HealthIEEE Reviews in Biomedical Engineering10.1109/RBME.2018.285150012(319-332)Online publication date: 2019
https://doi.org/10.1109/RBME.2018.2851500
Srivastava NNewn JVelloso E(2018)Combining Low and Mid-Level Gaze Features for Desktop Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32870672:4(1-27)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1145/3287067
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Index Terms

Quantifying community mobility after stroke using mobile phone technology
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics

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Published In

UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

September 2017

1089 pages

ISBN:9781450351904

DOI:10.1145/3123024

General Chairs:
Seungyon "Claire" Lee
Google
,
Leila Takayama
UC Santa Cruz
,
Khai Truong
University of Toronto

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2017

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National Institute on Disability, Independent Living, and Rehabilitation Research

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UbiComp '17

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UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 11 - 15, 2017

Hawaii, Maui

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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UBICOMP '24

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

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Cited By

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Sprint GCook DFritz R(2020)Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point DetectionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2020.2999607(1-1)Online publication date: 2020
https://doi.org/10.1109/JBHI.2020.2999607
Cook DSchmitter-Edgecombe MJonsson LMorant A(2019)Technology-Enabled Assessment of Functional HealthIEEE Reviews in Biomedical Engineering10.1109/RBME.2018.285150012(319-332)Online publication date: 2019
https://doi.org/10.1109/RBME.2018.2851500
Srivastava NNewn JVelloso E(2018)Combining Low and Mid-Level Gaze Features for Desktop Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32870672:4(1-27)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1145/3287067
Bo XPoellabauer CO'Brien MMummidisetty CJayaraman A(2018)Detecting Label Errors in Crowd-Sourced Smartphone Sensor Data2018 International Workshop on Social Sensing (SocialSens)10.1109/SocialSens.2018.00017(20-25)Online publication date: Apr-2018
https://doi.org/10.1109/SocialSens.2018.00017

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