Recognising Human Activity in free-living using multiple body-worn accelerometers IEEE 28th June .pdf (475.56 kB)

Recognizing human activity in free-living using multiple body-worn accelerometers

Download (475.56 kB)
journal contribution
posted on 13.12.2017, 11:03 by Elliott Fullerton, Ben Heller, Mario Munoz-Organero
© 2001-2012 IEEE. Recognizing human activity is very useful for an investigator about a patient's behavior and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity; however, research looking at the use of multiple body worn accelerometers in a free living environment to recognize a wide range of activities is not evident. This paper aimed to successfully recognize activity and sub-category activity types through the use of multiple body worn accelerometers in a free-living environment. Ten participants (Age = 23.1 ± 1.7 years, height =171.0 ± 4.7 cm, and mass = 78.2 ± 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and subcategory activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of preprocessing algorithms, feature, and classifier selections were tested, accuracy, and computing time were reported. A fine k-nearest neighbor classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities ( > 95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Results show that recognition of activity and sub-category activity types is possible in a free-living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living.

History

School

  • Sport, Exercise and Health Sciences

Published in

IEEE Sensors Journal

Volume

17

Issue

16

Pages

5290 - 5297

Citation

FULLERTON, E., HELLER, B. and MUNOZ-ORGANERO, M., 2017. Recognizing human activity in free-living using multiple body-worn accelerometers. IEEE Sensors Journal, 17(16), pp. 5290-5297.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2017

Notes

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

ISSN

1530-437X

Language

en

Exports