Three Methods for Energy-Efficient Context Recognition

Context recognition is a process where (usually wearable) sensors are used to determine the context (location, activity, etc.) of users wearing them. A major problems of such context- recognition systems is the high energy cost of collecting and processing sensor data. This paper summarizes a doctoral thesis that focuses on solving this problem by proposing a general methodology for increasing the energyefficiency of context-recognition systems. The thesis proposes and combines three different methods that can adapt a system's sensing settings based on the last recognized context and last seen sensor readings.