Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

6-2012

Abstract

We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our findings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over directly using statistical features.

Keywords

activity recognition, semantic activities, accelerometer, NCCR-MICS, NCCR-MICS/ESDM

Discipline

Software Engineering

Research Areas

Software Systems

Publication

International Symposium on Wearable Computers (ISWC)

First Page

37

Last Page

40

ISBN

9781467315838

Identifier

10.1109/ISWC.2012.22

Publisher

IEEE

City or Country

Newcastle, UK

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/ISWC.2012.22

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