Connecting personal-scale sensing and networked community behavior to infer human activities

Nicholas D. Lane, Li Pengyu, Lin Zhou, Feng Zhao
2014 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct  
Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a large-scale. However, slower progress in being made in activity recognition which remains often unreliable in everyday environments. In this paper, we investigate how to leverage the increasing capacity to gather data at a population-scale towards improving existing models of human behavior. Specifically, we consider the various social phenomena and environmental factors that cause people to develop
more » ... related behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions) that shape decisions and options; and cases of adopted behavior, as people learn from each other or assume group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of these phenomena and recognize activities solely on the basis of sensor-data captured from a single individual. We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns of community behavior are mined to identify social connections that can cause correlated behavior, this information is used to augment sensor-based inferences available from the actions of individuals. Our evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four mobile sensing datasets that collectively require a diverse set of activities to be recognized.
doi:10.1145/2632048.2636094 dblp:conf/huc/LanePZZ14 fatcat:tpkci4jvz5gmdb3l7tl36xe6ya