We Know What You Are Doing or Going to Do: Towards Accurate Human Activities Sensing

Abstract

Understanding Activities of Human Daily Life is a fundamental and essential AI problem for Pervasive Computing and Human-Computer Interaction. Activity Sensing has attracted enormous research on activity recognition from mobile sensor data. However, there are two challenging problems: There is no standard taxonomy of activities and there is a lack of research on sensing high level activities. To this end, firstly, we built AHDL, the first knowledge base of Activities of Human Daily Life in this planet leveraging a large time use surveys. AHDL not only has a taxonomy of activities but also has common sense knowledge of these activities. Secondly, we designed ActivitySensor, a Conditional Random Fields based Sensor for sensing high level activities in AHDL. To be specific, ActivitySensor performs activity sensing using Conditional Random Fields model by combining contextual signals (time, location, previous activity and related person) and demographical signals. Extensive experiments demonstrated that ActivitySensor can improve the accuracy of activity recognition about 15% comparing to state-of-the-art methods on the same dataset. What's more, we revealed that ActivitySensor can predict what will you do next with high accuracy.