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| Content Provider | ACM Digital Library |
|---|---|
| Author | Wilson, Daniel H. Atkeson, Chris Long, Anna C. |
| Abstract | Identifying what people do in the home can both inform ubiquitous computing application design decisions and provide training data to the machine learning algorithms used in their implementation. This paper describes an unsupervised technique in which contextual information gathered by ubiquitous sensors is used to help users label a multitude of anonymous activity episodes. This context-aware recognition survey is a game-like computer program in which users attempt to correctly guess which activity is happening after seeing a series of symbolic images that represent sensor values generated during the activity. We report a user study of the system, focusing on how well subjects were able to recognize their own activities, the activities of others, and counterfeits that did not correspond to any activity. |
| Starting Page | 1865 |
| Ending Page | 1868 |
| Page Count | 4 |
| File Format | |
| ISBN | 1595930027 |
| DOI | 10.1145/1056808.1057042 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2005-04-02 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Experience sampling Recall survey Activity recognition Ubiquitous computing Context-aware computing Home |
| Content Type | Text |
| Resource Type | Article |
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