Delivering accurate and opportune information on people's activities and behaviors has become one of the most important tasks within pervasive computing. Its wide spectrum of potential applications in medical, entertainment, and tactical scenarios, motivates further
research and development of new strategies to improve accuracy, pervasiveness, and eciency.
This dissertation addresses the recognition of human activities (HAR) with wearable sensors in three main regards: In the rst place, physiological signals have been incorporated as a new source of information to improve the recognition accuracy achieved by conventional approaches, which rely on accelerometer signals solely. A new HAR system, Centinela, was born from such concept, employing structural feature extraction along with classier
ensembles, and achieving over 95% of recognition accuracy.
In the second place, real time activity recognition was enabled by Vigilante, a mobile HAR framework under the AndroidTM platform. Providing immediate feedback on the user's activities is especially benecial in healthcare and military applications, which
may require alert triggering or support of decision making. The evaluation demonstrates that Vigilante is energy ecient while maintaining high accuracy (i.e., up to 96.8%) and
low response time. The system features MECLA, a mobile library for the evaluation of classification algorithms, which is also suitable for further machine learning applications.
Finally, the activity recognition accuracy is improved by two new strategies for decision fusion and selection in multiple classier systems: the failure product and the precision-recall dierence. The experimental analysis conrms that the presented methods are benecial, not only for recognizing human activities, but also for many other classication problems.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-5316 |
| Date | 01 January 2012 |
| Creators | Lara Yejas, Oscar David |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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