Year: 2014
Type: dissertation
Abstract: In this thesis we develop novel methods for continuous and sustained context inference on mobile platforms. We address challenges present in real-world deployment of two popular context recognition tasks within ubiquitous computing and mobile sensing, namely localization and activity recognition. In the first part of the thesis, we provide a new localization algorithm for mobile devices using the existing GSM communication infrastructures, and then propose a solution for energy-efficient and robust tracking on mobile devices that are equipped with sensors such as GPS, compass, and accelerometer. In the second part of the thesis we propose a novel sparse-coding-based activity recognition framework that mitigates the time-consuming and costly bootstrapping process of activity recognizers employing supervised learning. The framework uses a vast amount of unlabeled data to automatically learn a sensor data representation through a set of extracted characteristic patterns and generalizes well across activity domains and sensor modalities.
Source: Työväentutkimus Vuosikirja
Author: Sourav Bhattacharya
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Helsingin Yliopisto
OpenAlex ID: W2123400599