Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study

Jicheng Fu, Tao Liu, Maria Jones, Gang Qian, Yih Kuen Jan

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

Abstract

A wheelchair user's activity and mobility level is an important indicator of his/her quality of life and health status. To assess the activity and mobility level, wheelchair maneuvering data must be captured and analyzed. Recently, the inertial sensors, such as accelerometers, have been used to collect wheelchair maneuvering data. However, these sensors are sensitive to noises, which can lead to inaccurate analysis results. In this study, we analyzed the characteristics of wheelchair maneuvering data and developed a novel machine-learning algorithm, which could classify wheelchair maneuvering data into a series of wheelchair maneuvers. The use of machine-learning techniques empowers our approach to tolerate noises by capturing the patterns of wheelchair maneuvers. Experimental results showed that the proposed algorithm could accurately classify wheelchair maneuvers into eight classes, i.e., stationary, linear acceleration/deceleration, linear constant speed, left/right turns, and left/right spot turns. The fine-grained analysis on wheelchair maneuvering data can depict a more comprehensive picture of wheelchair users' activity and mobility levels, and enable the quantitative analysis of their quality of life and health status.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1731-1734
Number of pages4
ISBN (Electronic)9781424479290
DOIs
StatePublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Publication series

Name2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

Fingerprint

Wheelchairs
Sensors
Health Status
Learning systems
Noise
Quality of Life
Health
Deceleration
Accelerometers
Learning algorithms

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)

Cite this

Fu, J., Liu, T., Jones, M., Qian, G., & Jan, Y. K. (2014). Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 1731-1734). [6943942] (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6943942

Characterization of wheelchair maneuvers based on noisy inertial sensor data : A preliminary study. / Fu, Jicheng; Liu, Tao; Jones, Maria; Qian, Gang; Jan, Yih Kuen.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1731-1734 6943942 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

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

Fu, J, Liu, T, Jones, M, Qian, G & Jan, YK 2014, Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6943942, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1731-1734, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6943942
Fu J, Liu T, Jones M, Qian G, Jan YK. Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1731-1734. 6943942. (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). https://doi.org/10.1109/EMBC.2014.6943942
Fu, Jicheng ; Liu, Tao ; Jones, Maria ; Qian, Gang ; Jan, Yih Kuen. / Characterization of wheelchair maneuvers based on noisy inertial sensor data : A preliminary study. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1731-1734 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).
@inproceedings{8574caf6229748b789f82e1f0c1d4c9e,
title = "Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study",
abstract = "A wheelchair user's activity and mobility level is an important indicator of his/her quality of life and health status. To assess the activity and mobility level, wheelchair maneuvering data must be captured and analyzed. Recently, the inertial sensors, such as accelerometers, have been used to collect wheelchair maneuvering data. However, these sensors are sensitive to noises, which can lead to inaccurate analysis results. In this study, we analyzed the characteristics of wheelchair maneuvering data and developed a novel machine-learning algorithm, which could classify wheelchair maneuvering data into a series of wheelchair maneuvers. The use of machine-learning techniques empowers our approach to tolerate noises by capturing the patterns of wheelchair maneuvers. Experimental results showed that the proposed algorithm could accurately classify wheelchair maneuvers into eight classes, i.e., stationary, linear acceleration/deceleration, linear constant speed, left/right turns, and left/right spot turns. The fine-grained analysis on wheelchair maneuvering data can depict a more comprehensive picture of wheelchair users' activity and mobility levels, and enable the quantitative analysis of their quality of life and health status.",
author = "Jicheng Fu and Tao Liu and Maria Jones and Gang Qian and Jan, {Yih Kuen}",
year = "2014",
month = "11",
day = "2",
doi = "10.1109/EMBC.2014.6943942",
language = "English (US)",
series = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1731--1734",
booktitle = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",
address = "United States",

}

TY - GEN

T1 - Characterization of wheelchair maneuvers based on noisy inertial sensor data

T2 - A preliminary study

AU - Fu, Jicheng

AU - Liu, Tao

AU - Jones, Maria

AU - Qian, Gang

AU - Jan, Yih Kuen

PY - 2014/11/2

Y1 - 2014/11/2

N2 - A wheelchair user's activity and mobility level is an important indicator of his/her quality of life and health status. To assess the activity and mobility level, wheelchair maneuvering data must be captured and analyzed. Recently, the inertial sensors, such as accelerometers, have been used to collect wheelchair maneuvering data. However, these sensors are sensitive to noises, which can lead to inaccurate analysis results. In this study, we analyzed the characteristics of wheelchair maneuvering data and developed a novel machine-learning algorithm, which could classify wheelchair maneuvering data into a series of wheelchair maneuvers. The use of machine-learning techniques empowers our approach to tolerate noises by capturing the patterns of wheelchair maneuvers. Experimental results showed that the proposed algorithm could accurately classify wheelchair maneuvers into eight classes, i.e., stationary, linear acceleration/deceleration, linear constant speed, left/right turns, and left/right spot turns. The fine-grained analysis on wheelchair maneuvering data can depict a more comprehensive picture of wheelchair users' activity and mobility levels, and enable the quantitative analysis of their quality of life and health status.

AB - A wheelchair user's activity and mobility level is an important indicator of his/her quality of life and health status. To assess the activity and mobility level, wheelchair maneuvering data must be captured and analyzed. Recently, the inertial sensors, such as accelerometers, have been used to collect wheelchair maneuvering data. However, these sensors are sensitive to noises, which can lead to inaccurate analysis results. In this study, we analyzed the characteristics of wheelchair maneuvering data and developed a novel machine-learning algorithm, which could classify wheelchair maneuvering data into a series of wheelchair maneuvers. The use of machine-learning techniques empowers our approach to tolerate noises by capturing the patterns of wheelchair maneuvers. Experimental results showed that the proposed algorithm could accurately classify wheelchair maneuvers into eight classes, i.e., stationary, linear acceleration/deceleration, linear constant speed, left/right turns, and left/right spot turns. The fine-grained analysis on wheelchair maneuvering data can depict a more comprehensive picture of wheelchair users' activity and mobility levels, and enable the quantitative analysis of their quality of life and health status.

UR - http://www.scopus.com/inward/record.url?scp=84929492638&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929492638&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2014.6943942

DO - 10.1109/EMBC.2014.6943942

M3 - Conference contribution

C2 - 25570310

AN - SCOPUS:84929492638

T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

SP - 1731

EP - 1734

BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -