Postural transition detection using a wireless sensor activity monitoring system

Richelle LeMay, Sangil Choi, Jong-Hoon Youn, Jay Newstorm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.

Original languageEnglish (US)
Title of host publicationGrid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings
Pages393-402
Number of pages10
DOIs
StatePublished - Sep 9 2013
Event8th International Conference on Grid and Pervasive Computing, GPC 2013 - Seoul, Korea, Republic of
Duration: May 9 2013May 11 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Grid and Pervasive Computing, GPC 2013
CountryKorea, Republic of
CitySeoul
Period5/9/135/11/13

Fingerprint

Wireless Sensors
Monitoring System
Health
Monitoring
Sensors
Accelerometers
Human Error
Accelerometer
Classification Algorithm
Energy Efficient
Person

Keywords

  • activity classification
  • mobility monitoring
  • sensor networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

LeMay, R., Choi, S., Youn, J-H., & Newstorm, J. (2013). Postural transition detection using a wireless sensor activity monitoring system. In Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings (pp. 393-402). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7861 LNCS). https://doi.org/10.1007/978-3-642-38027-3_42

Postural transition detection using a wireless sensor activity monitoring system. / LeMay, Richelle; Choi, Sangil; Youn, Jong-Hoon; Newstorm, Jay.

Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings. 2013. p. 393-402 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7861 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

LeMay, R, Choi, S, Youn, J-H & Newstorm, J 2013, Postural transition detection using a wireless sensor activity monitoring system. in Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7861 LNCS, pp. 393-402, 8th International Conference on Grid and Pervasive Computing, GPC 2013, Seoul, Korea, Republic of, 5/9/13. https://doi.org/10.1007/978-3-642-38027-3_42
LeMay R, Choi S, Youn J-H, Newstorm J. Postural transition detection using a wireless sensor activity monitoring system. In Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings. 2013. p. 393-402. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38027-3_42
LeMay, Richelle ; Choi, Sangil ; Youn, Jong-Hoon ; Newstorm, Jay. / Postural transition detection using a wireless sensor activity monitoring system. Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings. 2013. pp. 393-402 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{7b3fa1ba32df4300818c2365fd769975,
title = "Postural transition detection using a wireless sensor activity monitoring system",
abstract = "Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90{\%} six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.",
keywords = "activity classification, mobility monitoring, sensor networks",
author = "Richelle LeMay and Sangil Choi and Jong-Hoon Youn and Jay Newstorm",
year = "2013",
month = "9",
day = "9",
doi = "10.1007/978-3-642-38027-3_42",
language = "English (US)",
isbn = "9783642380266",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "393--402",
booktitle = "Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings",

}

TY - GEN

T1 - Postural transition detection using a wireless sensor activity monitoring system

AU - LeMay, Richelle

AU - Choi, Sangil

AU - Youn, Jong-Hoon

AU - Newstorm, Jay

PY - 2013/9/9

Y1 - 2013/9/9

N2 - Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.

AB - Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.

KW - activity classification

KW - mobility monitoring

KW - sensor networks

UR - http://www.scopus.com/inward/record.url?scp=84883387809&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883387809&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-38027-3_42

DO - 10.1007/978-3-642-38027-3_42

M3 - Conference contribution

AN - SCOPUS:84883387809

SN - 9783642380266

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

SP - 393

EP - 402

BT - Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings

ER -