A Device-Orientation Independent Method for Activity Recognition

This paper describes an orientation-independent method for detecting activities of daily living based on reference coordinate transformation. With the proposed method, a classification model can be trained using data acquired during a specific sensor orientation and applied to other input signals re...

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Bibliographic Details
Published in2010 International Conference on Body Sensor Networks pp. 19 - 23
Main Author Thiemjarus, Surapa
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Accelerometers
Body sensor networks
context sensing
device orientation
Intelligent sensors
Legged locomotion
Medical services
Motion analysis
recognition
Sensor phenomena and characterization
Sensor systems
subject-independent model
Wearable computers
Wearable sensors
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Summary:This paper describes an orientation-independent method for detecting activities of daily living based on reference coordinate transformation. With the proposed method, a classification model can be trained using data acquired during a specific sensor orientation and applied to other input signals regardless of the orientation of the device. The technique is validated using activity recognition experiments with four different orientations of a single tri-axial accelerometer placed on the waist of 13 subjects performing a sub-class of activities of daily living. A high subject-independent accuracy of 90.42% has been achieved, reflecting a significant improvement of 11.74% and 16.58%, compared with classification without input transformation and classification with orientation-specific models, respectively.
ISBN:142445817X
9781424458172
ISSN:2376-8886
DOI:10.1109/BSN.2010.55