Abstract
With the rapid progress of the MEMs process, the cost and the size of accelerometers are reducing rapidly. As a result, accelerometers have found many new applications in industrial, entertainment and medical domains. One of such an applications is to acquire information about human body movement.
The objective of this work is to use knee acceleration signal for indentity verification. Comparing with traditional biometric methods, this approach has several distinct features. First, it can aquire a large amount of data efficiently and conventiently. Second, it is relatively difficult to duplicate. In designing the verification algorithm, this study has developed a neural network method a hyperspherical classifier method. The experimental results demonstrated that hyperspherical classifier provide better performances in this application. By setting the sensitively to 85%, the specificity achieved by the hyperspherical classifier is at least 95%.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0721108-160528 |
| Date | 21 July 2008 |
| Creators | Chen, Po-ju |
| Contributors | Pei-Chung Chen, Chen-wen Yen, Ming-Huei Yu |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528 |
| Rights | unrestricted, Copyright information available at source archive |
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