Publication Type

Conference Proceeding Article

Version

acceptedVersion

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 and Cyber-Physical Systems

Publication

16th International Symposium on Wearable Computers ISWC 2012: Newcastle, 18-22 June: Proceedings

First Page

37

Last Page

40

ISBN

9781467315838

Identifier

10.1109/ISWC.2012.22

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ISWC.2012.22

Download

Share

COinS