Activity Sense Organs: Energy-efficient Activity Sensing with Adaptive Duty Cycle Control

Abstract

Recently, smartphones with built-in sensors are exploited to detect human activities. Since sensing human activities may exhaust energy of a smartphone, this work brings up the concept of "activity sense organs" to the uprising smartphone sensing paradigm. As the human sense organs are the most important sensors to perceive real-world information, this work identifies the most important sensors to perceive a particular human activity based on the "quality of the features" contributed by sensors. To optimize the energy usage of activity sensing, we exploit the importance of sensors to profile the duty-cycles of sensors for activity detection. Specifically, a smartphone will operate in a duty-cycled sensing mode, where sensors in the activity sense organs will collect data at the higher sampling rate than those sensors not in the activity sense organs. We design a knowledge lifecycle to find out the activity sense organs, profile the sensing modes of activity detection, and adjust the sensing mode for real-time activity detection. The experimental results indicate that the activity sense organs provide sufficient information for activity detection.