Validation of automated mobility assessment using a single 3D sensor

Jiun Yu Kao; Minh Nguyen; Luciano Nocera; Cyrus Shahabi; Antonio Ortega; Carolee Winstein; Ibrahim Sorkhoh; Yu Chen Chung; Yi An Chen; Helen Bacon

doi:10.1007/978-3-319-48881-3_12

Validation of automated mobility assessment using a single 3D sensor

Jiun Yu Kao, Minh Nguyen, Luciano Nocera, Cyrus Shahabi, Antonio Ortega, Carolee Winstein, Ibrahim Sorkhoh, Yu Chen Chung, Yi An Chen, Helen Bacon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson’s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches.We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.

Original language	English (US)
Title of host publication	Computer Vision – ECCV 2016 Workshops, Proceedings
Editors	Gang Hua, Herve Jegou
Publisher	Springer Verlag
Pages	162-177
Number of pages	16
ISBN (Print)	9783319488806
DOIs	https://doi.org/10.1007/978-3-319-48881-3_12
State	Published - 2016
Externally published	Yes
Event	Computer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands Duration: Oct 8 2016 → Oct 16 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9914 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Computer Vision - ECCV 2016 Workshops, Proceedings
Country/Territory	Netherlands
City	Amsterdam
Period	10/8/16 → 10/16/16

Keywords

3D sensor
Classification
Human performance
Mobility assessment
Parkinson’s disease

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-48881-3_12

Cite this

Kao, J. Y., Nguyen, M., Nocera, L., Shahabi, C., Ortega, A., Winstein, C., Sorkhoh, I., Chung, Y. C., Chen, Y. A., & Bacon, H. (2016). Validation of automated mobility assessment using a single 3D sensor. In G. Hua, & H. Jegou (Eds.), Computer Vision – ECCV 2016 Workshops, Proceedings (pp. 162-177). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_12

Validation of automated mobility assessment using a single 3D sensor. / Kao, Jiun Yu; Nguyen, Minh; Nocera, Luciano et al.
Computer Vision – ECCV 2016 Workshops, Proceedings. ed. / Gang Hua; Herve Jegou. Springer Verlag, 2016. p. 162-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kao, JY, Nguyen, M, Nocera, L, Shahabi, C, Ortega, A, Winstein, C, Sorkhoh, I, Chung, YC, Chen, YA & Bacon, H 2016, Validation of automated mobility assessment using a single 3D sensor. in G Hua & H Jegou (eds), Computer Vision – ECCV 2016 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9914 LNCS, Springer Verlag, pp. 162-177, Computer Vision - ECCV 2016 Workshops, Proceedings, Amsterdam, Netherlands, 10/8/16. https://doi.org/10.1007/978-3-319-48881-3_12

Kao JY, Nguyen M, Nocera L, Shahabi C, Ortega A, Winstein C et al. Validation of automated mobility assessment using a single 3D sensor. In Hua G, Jegou H, editors, Computer Vision – ECCV 2016 Workshops, Proceedings. Springer Verlag. 2016. p. 162-177. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-48881-3_12

Kao, Jiun Yu ; Nguyen, Minh ; Nocera, Luciano et al. / Validation of automated mobility assessment using a single 3D sensor. Computer Vision – ECCV 2016 Workshops, Proceedings. editor / Gang Hua ; Herve Jegou. Springer Verlag, 2016. pp. 162-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b16e1a9b345147cea5e0e068d6dd83f4,

title = "Validation of automated mobility assessment using a single 3D sensor",

abstract = "Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson{\textquoteright}s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches.We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.",

keywords = "3D sensor, Classification, Human performance, Mobility assessment, Parkinson{\textquoteright}s disease",

author = "Kao, {Jiun Yu} and Minh Nguyen and Luciano Nocera and Cyrus Shahabi and Antonio Ortega and Carolee Winstein and Ibrahim Sorkhoh and Chung, {Yu Chen} and Chen, {Yi An} and Helen Bacon",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; Computer Vision - ECCV 2016 Workshops, Proceedings ; Conference date: 08-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-48881-3_12",

language = "English (US)",

isbn = "9783319488806",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "162--177",

editor = "Gang Hua and Herve Jegou",

booktitle = "Computer Vision – ECCV 2016 Workshops, Proceedings",

}

TY - GEN

T1 - Validation of automated mobility assessment using a single 3D sensor

AU - Kao, Jiun Yu

AU - Nguyen, Minh

AU - Nocera, Luciano

AU - Shahabi, Cyrus

AU - Ortega, Antonio

AU - Winstein, Carolee

AU - Sorkhoh, Ibrahim

AU - Chung, Yu Chen

AU - Chen, Yi An

AU - Bacon, Helen

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson’s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches.We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.

AB - Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson’s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches.We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.

KW - 3D sensor

KW - Classification

KW - Human performance

KW - Mobility assessment

KW - Parkinson’s disease

UR - http://www.scopus.com/inward/record.url?scp=84996879811&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996879811&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-48881-3_12

DO - 10.1007/978-3-319-48881-3_12

M3 - Conference contribution

AN - SCOPUS:84996879811

SN - 9783319488806

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 162

EP - 177

BT - Computer Vision – ECCV 2016 Workshops, Proceedings

A2 - Hua, Gang

A2 - Jegou, Herve

PB - Springer Verlag

T2 - Computer Vision - ECCV 2016 Workshops, Proceedings

Y2 - 8 October 2016 through 16 October 2016

ER -

Validation of automated mobility assessment using a single 3D sensor

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this