Assessing the utility of smart mobile phones in gait pattern analysis

Mingjing Yang, Huiru Zheng, Haiying Wang, Sally McClean, Nigel Harris

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

This paper aims to study the feasibility of using a smart mobile phone with an embedded accelerometer in gait pattern monitoring. The second motivation is to examine the impact of the accelerometer sampling frequency on gait analysis. A mobile phone and a standalone accelerometer sensor were simultaneously attached to subject's lower back to record walking patterns. The degree of agreement between gait features derived from two devices was assessed in terms of average error rate, normalised limits of agreement and intra-class correlation. Various agreement levels were observed for three temporal features, three root mean square features, five regularity features and two symmetry features. The downsampling data were used to examine the impact of sample intervals on the gait features. Eleven out of 13 features have normalised mean difference less than 0.1 when sample intervals were less than 50ms. To carry out a further evaluation, the features derived from the downsampling gait data were used to classify subjects with chronic pain and health subjects, and a classification accuracy of 90% was achieved. The results showed that it is feasible and reliable to assess and monitor gait patterns based on spatio-temporal gait features derived from smart mobile phones with an embedded accelerometer.
Original languageEnglish
Pages (from-to)81-88
Number of pages8
JournalHealth and Technology
Volume2
Issue number1
DOIs
Publication statusPublished - 2012

Fingerprint

Cell Phones
Mobile phones
Gait
Accelerometers
Gait analysis
Health
Sampling
Feasibility Studies
Smartphone
Monitoring
Sensors
Chronic Pain
Walking
Equipment and Supplies

Cite this

Assessing the utility of smart mobile phones in gait pattern analysis. / Yang, Mingjing; Zheng, Huiru; Wang, Haiying; McClean, Sally; Harris, Nigel.

In: Health and Technology, Vol. 2, No. 1, 2012, p. 81-88.

Research output: Contribution to journalArticle

Yang, M, Zheng, H, Wang, H, McClean, S & Harris, N 2012, 'Assessing the utility of smart mobile phones in gait pattern analysis', Health and Technology, vol. 2, no. 1, pp. 81-88. https://doi.org/10.1007/s12553-012-0021-8
Yang, Mingjing ; Zheng, Huiru ; Wang, Haiying ; McClean, Sally ; Harris, Nigel. / Assessing the utility of smart mobile phones in gait pattern analysis. In: Health and Technology. 2012 ; Vol. 2, No. 1. pp. 81-88.
@article{b56690e274cc48b796ca04bf32936aec,
title = "Assessing the utility of smart mobile phones in gait pattern analysis",
abstract = "This paper aims to study the feasibility of using a smart mobile phone with an embedded accelerometer in gait pattern monitoring. The second motivation is to examine the impact of the accelerometer sampling frequency on gait analysis. A mobile phone and a standalone accelerometer sensor were simultaneously attached to subject's lower back to record walking patterns. The degree of agreement between gait features derived from two devices was assessed in terms of average error rate, normalised limits of agreement and intra-class correlation. Various agreement levels were observed for three temporal features, three root mean square features, five regularity features and two symmetry features. The downsampling data were used to examine the impact of sample intervals on the gait features. Eleven out of 13 features have normalised mean difference less than 0.1 when sample intervals were less than 50ms. To carry out a further evaluation, the features derived from the downsampling gait data were used to classify subjects with chronic pain and health subjects, and a classification accuracy of 90{\%} was achieved. The results showed that it is feasible and reliable to assess and monitor gait patterns based on spatio-temporal gait features derived from smart mobile phones with an embedded accelerometer.",
author = "Mingjing Yang and Huiru Zheng and Haiying Wang and Sally McClean and Nigel Harris",
year = "2012",
doi = "10.1007/s12553-012-0021-8",
language = "English",
volume = "2",
pages = "81--88",
journal = "Health and Technology",
issn = "2190-7188",
publisher = "Springer Verlag",
number = "1",

}

TY - JOUR

T1 - Assessing the utility of smart mobile phones in gait pattern analysis

AU - Yang, Mingjing

AU - Zheng, Huiru

AU - Wang, Haiying

AU - McClean, Sally

AU - Harris, Nigel

PY - 2012

Y1 - 2012

N2 - This paper aims to study the feasibility of using a smart mobile phone with an embedded accelerometer in gait pattern monitoring. The second motivation is to examine the impact of the accelerometer sampling frequency on gait analysis. A mobile phone and a standalone accelerometer sensor were simultaneously attached to subject's lower back to record walking patterns. The degree of agreement between gait features derived from two devices was assessed in terms of average error rate, normalised limits of agreement and intra-class correlation. Various agreement levels were observed for three temporal features, three root mean square features, five regularity features and two symmetry features. The downsampling data were used to examine the impact of sample intervals on the gait features. Eleven out of 13 features have normalised mean difference less than 0.1 when sample intervals were less than 50ms. To carry out a further evaluation, the features derived from the downsampling gait data were used to classify subjects with chronic pain and health subjects, and a classification accuracy of 90% was achieved. The results showed that it is feasible and reliable to assess and monitor gait patterns based on spatio-temporal gait features derived from smart mobile phones with an embedded accelerometer.

AB - This paper aims to study the feasibility of using a smart mobile phone with an embedded accelerometer in gait pattern monitoring. The second motivation is to examine the impact of the accelerometer sampling frequency on gait analysis. A mobile phone and a standalone accelerometer sensor were simultaneously attached to subject's lower back to record walking patterns. The degree of agreement between gait features derived from two devices was assessed in terms of average error rate, normalised limits of agreement and intra-class correlation. Various agreement levels were observed for three temporal features, three root mean square features, five regularity features and two symmetry features. The downsampling data were used to examine the impact of sample intervals on the gait features. Eleven out of 13 features have normalised mean difference less than 0.1 when sample intervals were less than 50ms. To carry out a further evaluation, the features derived from the downsampling gait data were used to classify subjects with chronic pain and health subjects, and a classification accuracy of 90% was achieved. The results showed that it is feasible and reliable to assess and monitor gait patterns based on spatio-temporal gait features derived from smart mobile phones with an embedded accelerometer.

UR - http://dx.doi.org/10.1007/s12553-012-0021-8

UR - http://www.scopus.com/inward/record.url?scp=84863347086&partnerID=8YFLogxK

U2 - 10.1007/s12553-012-0021-8

DO - 10.1007/s12553-012-0021-8

M3 - Article

VL - 2

SP - 81

EP - 88

JO - Health and Technology

JF - Health and Technology

SN - 2190-7188

IS - 1

ER -