Year: 2015
Type: article
Abstract: This paper describes the design, implementation, and evaluation of Amoeba, a context-sensitive context detection service for mobile devices. Amoeba exports an API that allows a client to express interest in one or more context types (activity, indoor/outdoor, and entry/exit to/from named regions), subscribe to specific modes within each context (e.g., "walking" or "running", but no other activity), and specify a response latency (i.e., how often the client is notified). Each context has a detector that returns its estimate of the mode. The detectors take both the desired subscriptions and the current context detection into account, adjusting both the types of sensors and the sampling rates to achieve high accuracy and low energy consumption. We have implemented Amoeba on Android. Experiments with Amoeba on 45+ hours of data show that our activity detector achieves an accuracy between 92% and 99%, outperforming previous proposals like UCLA* (59%), EEMSS (82%) and SociableSense (72%), while consuming 4 to 6× less energy.
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1930464860