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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Minnen, D. Starner, T. Ward, J.A. Lukowicz, P. Troster, G. |
| Copyright Year | 2005 |
| Description | Author affiliation: Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA (Minnen, D.; Starner, T.) |
| Abstract | We describe our initial efforts to learn high-level human behaviors from low-level gestures observed using on-body sensors. Such an activity discovery system could be used to index captured journals of a person's life automatically. In a medical context, an annotated journal could assist therapists in helping to describe and treat symptoms characteristic to behavioral syndromes such as autism. We review our current work on user-independent activity recognition from continuous data where we identify "interesting" user gestures through a combination of acceleration and audio sensors placed on the user's wrists and elbows. We examine an algorithm that can take advantage of such a sensor framework to automatically discover and label recurring behaviors, and we suggest future work where correlations of these low-level gestures may indicate higher-level activities |
| Starting Page | 1545 |
| Ending Page | 1548 |
| File Size | 297647 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780393317 |
| DOI | 10.1109/ICME.2005.1521728 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-07-06 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Wearable sensors Wearable computers Sensor systems Accelerometers Sensor phenomena and characterization Medical treatment Legged locomotion Educational institutions Autism |
| Content Type | Text |
| Resource Type | Article |
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