Development of an automated physical activity classification application for mobile phones

Xia, Ying, Cheung, Vivian, Garcia, Elsa, Ding, Hang and Karunaithi, Mohan (2011). Development of an automated physical activity classification application for mobile phones. In: David P. Hansen, Anthony J. Maeder and Louise K. Schaper, Health Informatics: The Transformative Power of Innovation. Selected Papers from the 19th Australian National Health Informatics Conference (HIC 2011). HIC 2011: 19th Australian National Health Informatics Conference, Brisbane, QLD, Australia, (188-194). 1-5 August, 2011. doi:10.3233/978-1-60750-791-8-188

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Author Xia, Ying
Cheung, Vivian
Garcia, Elsa
Ding, Hang
Karunaithi, Mohan
Title of paper Development of an automated physical activity classification application for mobile phones
Conference name HIC 2011: 19th Australian National Health Informatics Conference
Conference location Brisbane, QLD, Australia
Conference dates 1-5 August, 2011
Proceedings title Health Informatics: The Transformative Power of Innovation. Selected Papers from the 19th Australian National Health Informatics Conference (HIC 2011)   Check publisher's open access policy
Journal name Studies in Health Technology and Informatics   Check publisher's open access policy
Place of Publication Amsterdam, Netherlands
Publisher I O S Press
Publication Year 2011
Sub-type Fully published paper
DOI 10.3233/978-1-60750-791-8-188
Open Access Status
ISBN 9781607507901
9781607507918
ISSN 0926-9630
1879-8365
Editor David P. Hansen
Anthony J. Maeder
Louise K. Schaper
Volume 168
Start page 188
End page 194
Total pages 7
Language eng
Formatted Abstract/Summary
Background. Physical activity classification is an objective approach to assess levels of physical activity, and indicates an individual's degree of functional ability. It is significant for a number of the disciplines, such as behavioural sciences, physiotherapy, etc. Accelerometry is found to be a practical and low cost method for activity classification that could provide an objective and efficient measurement of people's daily activities.

Methods. This paper utilises a mobile phone with a built-in tri-axial accelerometer sensor to automatically classify normal physical activities. A rule-based activity classification model, which can recognise 4 common daily activities (lying, walking, sitting, and standing) and 6 transitions between postural orientations, is introduced here. In this model, three types of statuses (walking/transition, lying, and sitting/standing) are first classified based on the kinetic energy and upright angle. Transitions are then separated from walking and assigned to the corresponding type using upright angle algorithm. To evaluate the performance of this developed application, a trial is designed with 8 healthy adult subjects, who are required to perform a 6-minute activity routine with an iPhone fixed at the waist position.

Results. Based on the evaluation result, our application measures the length of time of each activity accurately and the achieved sensitivity of each activity classification exceeds 90% while the achieved specificity exceeds 96%. Meanwhile, regarding the transition identification, the sensitivities are high in stand-to-sit (80%) and low in sit-to-stand (56%).
Subjects 2204 Religion and Religious Studies
2718 Health Informatics
3605 Health Information Management
Keyword Activities of daily living
Cellular phone
Classification
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Conference Paper
Collection: School of Medicine Publications
 
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